CN113239889A

CN113239889A - Image recognition method, device, equipment, storage medium and computer program product

Info

Publication number: CN113239889A
Application number: CN202110632075.1A
Authority: CN
Inventors: 刘正义; 曹杨
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-06-07
Filing date: 2021-06-07
Publication date: 2021-08-10

Abstract

The present disclosure discloses an image recognition method, apparatus, device, storage medium and computer program product, relating to the technical field of artificial intelligence, in particular to the field of computer vision. One embodiment of the method comprises: acquiring an image to be identified; in response to receiving an identification operation for an image to be identified, determining a target object in the image to be identified; and acquiring and outputting the identification result corresponding to the target object. According to the embodiment, the target object in the image to be recognized can be determined firstly, and then the recognition result corresponding to the target object is obtained and output, so that richer content information is shown for the user.

Description

Image recognition method, device, equipment, storage medium and computer program product

Technical Field

The disclosed embodiments relate to the field of computers, in particular to the technical field of artificial intelligence such as computer vision, and in particular to an image recognition method, an image recognition device, image recognition equipment, a storage medium, and a computer program product.

Background

With the continuous development of mobile internet technology and internet of things technology and the change and improvement of concepts of people, the image recognition technology is widely applied, and has wide application prospects in various fields such as safety, finance, human-computer interaction, information, education and the like.

In the field of image recognition technology, a trained image recognition model is usually used to recognize an object in an image and obtain a recognition result. However, the obtained recognition result often includes only fixed information such as the name of the target object, and the information has no way to meet the user's requirement.

Disclosure of Invention

The embodiment of the disclosure provides an image identification method, an image identification device, an image identification equipment, a storage medium and a computer program product.

In a first aspect, an embodiment of the present disclosure provides an image recognition method, including: acquiring an image to be identified; in response to receiving an identification operation for an image to be identified, determining a target object in the image to be identified; and acquiring and outputting the identification result corresponding to the target object.

In a second aspect, an embodiment of the present disclosure provides an image recognition apparatus, including: a first acquisition module configured to acquire an image to be recognized; a determination module configured to determine a target object in an image to be recognized in response to receiving a recognition operation for the image to be recognized; and the first output module is configured to acquire and output a recognition result corresponding to the target object.

In a third aspect, an embodiment of the present disclosure provides a server, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in any one of the implementations of the first aspect.

In a fourth aspect, the disclosed embodiments propose a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in any one of the implementations of the first aspect.

In a fifth aspect, the present disclosure provides a computer program product including a computer program, which when executed by a processor implements the method as described in any implementation manner of the first aspect.

In a sixth aspect, an embodiment of the present disclosure provides an image recognition system, including: an image recognition system comprising: a terminal device and a server as described in the third aspect; a terminal device, comprising: the imaging device is used for acquiring an image to be identified; the network communication module is used for sending the image to be recognized to the server and receiving a recognition result which is output by the server and corresponds to a target object in the image to be recognized; and the display device is used for displaying the image to be identified and the identification result.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

Other features, objects, and advantages of the disclosure will become apparent from a reading of the following detailed description of non-limiting embodiments which proceeds with reference to the accompanying drawings. The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is an exemplary system architecture diagram in which the present disclosure may be applied;

FIG. 2 is a flow chart of a first embodiment of an image recognition method according to the present disclosure;

FIG. 3 is a flow chart of a second embodiment of an image recognition method according to the present disclosure;

FIG. 4 is a flow chart of a third embodiment of an image recognition method according to the present disclosure;

FIG. 5 is a flow chart of a fourth embodiment of an image recognition method according to the present disclosure;

FIG. 6 is a flow chart of a fifth embodiment of an image recognition method according to the present disclosure;

FIG. 7 is a schematic block diagram of one embodiment of an image recognition device according to the present disclosure;

FIG. 8 is a block diagram of an electronic device for implementing an image recognition method of an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of an image recognition system for implementing an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the image recognition method or image recognition apparatus of the present disclosure may be applied.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send action information or the like. Various client applications, such as a camera application, etc., may be installed on the

terminal devices

101, 102, 103.

The

terminal apparatuses

101, 102, and 103 may be hardware or software. When the

terminal devices

101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the

terminal apparatuses

101, 102, 103 are software, they can be installed in the above-described electronic apparatuses. It may be implemented as multiple pieces of software or software modules, or as a single piece of software or software module. And is not particularly limited herein.

The server 105 may provide various services. For example, the server 105 may analyze and process the images to be recognized acquired from the

terminal apparatuses

101, 102, 103, and generate processing results (e.g., recognition results, etc.).

The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.

It should be noted that the image recognition method provided by the embodiment of the present disclosure is generally executed by the server 105, and accordingly, the image recognition apparatus is generally disposed in the server 105.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow 200 of a first embodiment of an image recognition method according to the present disclosure is shown. The image recognition method comprises the following steps:

step 201, acquiring an image to be identified.

In this embodiment, an executing subject of the image recognition method (for example, the server 105 shown in fig. 1) may acquire an image to be recognized, where the image to be recognized is an image that needs to be recognized in this embodiment. In this embodiment, the image to be recognized may be acquired by an image sensor, where the image sensor is a sensor capable of acquiring an image, such as a camera sensor, and the image to be recognized may also be acquired from a local file storing a large number of images.

It should be noted that the image to be recognized in this embodiment may be a human face image, an animal image, a plant image, or a building image, or may even be a dynamic image, which is not specifically limited in this disclosure.

Step 202, in response to receiving the recognition operation for the image to be recognized, determining a target object in the image to be recognized.

In the present embodiment, the execution subject described above may determine the target object in the image to be recognized in a case where the recognition operation for the image to be recognized is received. The identification operation may be that the user clicks an identification key on the terminal device to indicate that the image to be identified is to be identified, or that no other operation is performed within 5 seconds after the user uploads the image to be identified, and the image to be identified is automatically identified. After receiving the recognition operation for the image to be recognized, the executing body determines the target object in the image to be recognized.

It should be noted that, since the image to be recognized may include more than one object, when the image to be recognized includes a plurality of objects, and when performing a recognition operation on the image to be recognized, the execution subject needs to determine a target object in the image to be recognized, where the target object is the object to be recognized.

And step 203, acquiring and outputting a recognition result corresponding to the target object.

In this embodiment, the execution body may acquire and output a recognition result corresponding to the target object. For example, after determining the target object in the image to be recognized, the executing entity may recognize the target object to obtain information such as a name and a category of the target object, and then obtain a corresponding recognition result based on the obtained information such as the name and the category of the target object, where the recognition result may include introduction information of the target object and/or poetry matched with the target object, and finally, the executing entity may output the obtained recognition result corresponding to the target object.

The image identification method provided by the embodiment of the disclosure comprises the steps of firstly obtaining an image to be identified; then, in response to receiving the identification operation aiming at the image to be identified, determining a target object in the image to be identified; and finally, acquiring and outputting the identification result corresponding to the target object. The present disclosure provides an image recognition method, which can determine a target object in an image to be recognized first, and then acquire and output a recognition result corresponding to the target object, so that a user can obtain more information related to the target object.

With continued reference to fig. 3, fig. 3 shows a flow 300 of a second embodiment of an image recognition method according to the present disclosure. The image recognition method comprises the following steps:

step 301, acquiring an image to be identified.

Step 302, in response to receiving the recognition operation for the image to be recognized, determining a target object in the image to be recognized.

The steps 301-.

Step 303, acquiring attribute information of the user.

In this embodiment, an executing subject of the image recognition method (e.g., the server 105 shown in fig. 1) may acquire attribute information of the user, where the attribute information may include age information, gender information, and the like. The obtaining mode can be that the user fills in the relevant attribute information of the user in real time, and then the executing body obtains the information filled by the user; the obtaining mode can also be that the executing body collects the attribute information of all users in advance, stores the collected attribute information in the information base, and obtains the related attribute information from the information base when necessary.

It should be noted that, in the technical solution of the present disclosure, the acquisition, storage, application, and the like of the personal information of the related user all conform to the regulations of the relevant laws and regulations, and do not violate the common customs of the public order.

And step 304, determining a content information base matched with the user based on the attribute information.

In this embodiment, the execution subject may determine a content information library matching the user based on the attribute information obtained in step 303. For example, the execution subject may determine a content information base matching the user based on age information of the user, and the execution subject may determine a content information base matching the user based on gender information of the user. The corresponding content information base is set for the users with different ages and different sexes, so that the information in the content information base is more matched with the user identity, and more targeted information is displayed for the users.

In step 305, content information corresponding to the target object is acquired from the content information base as a recognition result.

In this embodiment, the execution subject may acquire content information corresponding to the target object from the content information library, and use the acquired content information as the recognition result. The content information obtained from the content information base may be the name and category information of the target object, may also be related introduction information of the target object, and may also be poems, songs, and the like corresponding to the target object, and finally, the obtained content information is used as the recognition result.

And step 306, outputting the identification result.

In this embodiment, the executing entity may output the recognition result obtained in step 305.

As can be seen from fig. 3, compared with the embodiment corresponding to fig. 2, in the image recognition method in the present embodiment, attribute information of a user is acquired, a content information base matching the user is determined based on the attribute information, and a recognition result corresponding to a target object is acquired from the content information base. According to the method, the identification result corresponding to the user attribute information is obtained and output, so that more targeted information can be generated aiming at the user attribute information on the basis of identifying the image, and more valuable information is provided for the user.

With continued reference to fig. 4, fig. 4 shows a flow 400 of a third embodiment of an image recognition method according to the present disclosure. The image recognition method comprises the following steps:

step 401, acquiring an image to be identified.

Step 402, in response to receiving an identification operation for an image to be identified, determining a target object in the image to be identified.

The steps 401-.

In response to the target object being an animate object, growth state information corresponding to the target object is obtained as a recognition result, step 403.

In the present embodiment, the execution subject of the image recognition method (for example, the server 105 shown in fig. 1) may acquire growth state information corresponding to the target object and use the growth state information as the recognition result in the case where the target object is a living object. Assuming that the target object is a plant, the execution body may acquire growth state information of the target object, and the growth state information may be when the target object blooms, when the target object comes to a fruit, or the like, and use the acquired growth state information as a recognition result.

As an example, when the target object is a quincunx, the growth state information thereof may include: the flower buds grow slowly in 12 months, the white or red flowers grow finally, the leaves grow after the flowers wither, and the flowers mature in 5-6 months in the next year.

Step 404, outputting the recognition result based on the time sequence.

In this embodiment, the execution subject may output the recognition result obtained in step 403 based on the time sequence.

As an example, the execution body may output the growth state information (recognition result) of the plum blossom based on the chronological order, the plum blossom grows in the form of flower buds in 12 months, the plum blossom grows in the form of flower buds … … by 1 month in the second year, and the plum blossom matures by 5 months in the second year, and the above process may be output in the form of characters or images arranged in the chronological order.

As can be seen from fig. 4, compared with the embodiment corresponding to fig. 3, the image recognition method in this embodiment can acquire the growth state information corresponding to the target object when the target object is a living object, and output the growth state information as a recognition result, thereby displaying the recognition result of the target object more vividly, and the display manner is more vivid and interesting and is more easily accepted by the user.

With continued reference to fig. 5, fig. 5 illustrates a flow 500 of a fourth embodiment of an image recognition method according to the present disclosure. The image recognition method comprises the following steps:

step 501, acquiring an image to be identified.

Step 502, in response to receiving the recognition operation for the image to be recognized, determining a target object in the image to be recognized.

The steps 501-502 are substantially the same as the steps 401-402 of the foregoing embodiment, and the specific implementation manner may refer to the foregoing description of the steps 401-402, which is not described herein again.

In response to the target object being an animal, determining category information of the target object, step 503.

In the present embodiment, the execution subject of the image recognition method (for example, the server 105 shown in fig. 1) may determine the category information of the target object in the case where the target object is an animal. For example, when the target object is a tiger, it can be determined that the category information thereof is mammalia macrofelines.

Step 504, feature information of the object corresponding to the category information is acquired and output.

In this embodiment, the execution body may output feature information of an object corresponding to the category information. For example, when the target object is a tiger, the obtained category information of the tiger is mammalia macrofelis, and the execution main body can obtain other objects corresponding to the category information "mammalia macrofelis", such as leopards, and can obtain feature information of the leopards, where the feature information may include information of species of leopards, habitat, lifestyle patterns, distribution range, and the like. Finally, the execution body may output the feature information.

As can be seen from fig. 5, compared with the embodiment corresponding to fig. 4, in the image recognition method in this embodiment, when the target object is an animal, the category information of the target object may be determined, and then the feature information of the object corresponding to the category information is acquired and output, so that richer and more vivid content is provided for the user, more relevant content is presented for the user, and the use experience of the user is improved.

With continued reference to fig. 6, fig. 6 shows a flow 600 of a fifth embodiment of an image recognition method according to the present disclosure. The image recognition method comprises the following steps:

step 601, acquiring an image to be identified.

Step 601 is substantially the same as step 501 in the foregoing embodiment, and the specific implementation manner may refer to the foregoing description of step 501, which is not described herein again.

Step 602, in response to receiving the overall recognition operation for the image to be recognized, acquiring the position information of each object in the image to be recognized.

In the present embodiment, an executing subject of the image recognition method (for example, the server 105 shown in fig. 1) may acquire, upon receiving the overall recognition operation for the image to be recognized, position information of each object in the image to be recognized, for example, the position information may be a distance of the object from a center position of the image to be recognized. Since more than one object may be included in the image to be recognized, in the case that the image to be recognized includes a plurality of objects, the executing body needs to determine the object to be recognized in the image to be recognized, that is, needs to determine the target object in the image to be recognized first.

Step 603, determining the target object based on the position information.

In the present embodiment, the execution subject described above may determine the target object based on the position information obtained in step 602. For example, an object closest to the center position of the image to be recognized may be determined as a target object in the image to be recognized.

Step 604, obtaining and outputting the recognition result corresponding to the target object.

In this embodiment, the execution body may acquire and output a recognition result corresponding to the target object. After determining the target object in the image to be recognized in step 603, the executing entity may obtain and output a recognition result corresponding to the target object, where the recognition result may include information such as a name and a category of the target object, and may further include information such as an introduction information of the target object and/or a poetry matched with the target object.

Step 605, obtain the current geographic location information.

In this embodiment, the executing body may obtain the current geographic location information, where the obtaining of the current geographic location information may be implemented by using the prior art, and is not described herein again.

Step 606, generating recommendation information based on the current geographic position information and the target object.

In this embodiment, the executing entity may generate recommendation information based on the current geographic location information obtained in step 605 and the target object. Specifically, the execution subject may find a first object having a certain correlation with the target object at the current geographic location or a neighboring area of the current geographic location according to the name or the category of the target object, where the certain correlation may refer to the same category as the target object or the same structure as the target object. And then, generating corresponding recommendation information, wherein the recommendation information can comprise the name, the category and the position information of the first object, so that a user can find a new object according to the recommendation information.

Step 607, outputting the recommendation information.

In this embodiment, the execution subject may output the recommendation information generated in step 606.

As can be seen from fig. 6, compared with the embodiment corresponding to fig. 5, the image recognition method in this embodiment highlights the step of determining the target object, so that the determined target object is more accurate; in addition, in the method, the recommendation information can be generated according to the current geographic position information and the target object, so that the user can unlock a new object near the current position, the user can acquire more new information, and the use experience of the user is enriched.

With further reference to fig. 7, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an image recognition apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.

As shown in fig. 7, the image recognition apparatus 700 of the present embodiment may include: a first obtaining module 701, a determining module 702 and a first outputting module 703. The first obtaining module 701 is configured to obtain an image to be identified; a determination module 702 configured to determine a target object in an image to be recognized in response to receiving a recognition operation for the image to be recognized; a first output module 703 configured to acquire and output a recognition result corresponding to the target object.

In the present embodiment, in image recognition apparatus 700: the specific processing of the first obtaining module 701, the determining module 702 and the first outputting module 703 and the technical effects thereof can refer to the related descriptions of step 201 and step 203 in the corresponding embodiment of fig. 2, which are not repeated herein.

In some optional implementations of this embodiment, the determining module includes: a first obtaining sub-module configured to obtain position information of each object in an image to be recognized in response to receiving a whole recognition operation for the image to be recognized; a first determination submodule configured to determine a target object based on the position information.

In some optional implementations of this embodiment, the first output module includes: a second obtaining sub-module configured to obtain attribute information of the user; a second determination sub-module configured to determine a content information base matching the user based on the attribute information; a third obtaining sub-module configured to obtain content information corresponding to the target object from the content information base as a recognition result; a first output submodule configured to output a recognition result.

In some optional implementations of this embodiment, the first output module includes: a fourth obtaining sub-module configured to obtain growth state information corresponding to the target object as a recognition result in response to the target object being an animate object; and a second output submodule configured to output the recognition result based on the time sequence.

In some optional implementations of this embodiment, the first output module further includes: a third determining sub-module configured to determine category information of the target object in response to the target object being an animal class; and the third output submodule is configured to acquire and output the characteristic information of the object corresponding to the category information.

In some optional implementations of this embodiment, the image recognition apparatus further includes: a second obtaining module configured to obtain current geographical location information; a generation module configured to generate recommendation information based on the current geographic location information and the target object; a second output module configured to output the recommendation information.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 8 illustrates a schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

Computing unit 801 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 801 executes the respective methods and processes described above, such as the image recognition method. For example, in some embodiments, the image recognition method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM 802 and/or communications unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the image recognition method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the image recognition method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service extensibility in the conventional physical host and Virtual Private Server (VPS) service.

With further reference to fig. 9, fig. 9 is a schematic structural diagram of an image recognition system for implementing an embodiment of the present disclosure, and as shown in fig. 9, the image recognition system 900 of the present embodiment may include: a terminal device 901 and a server 902.

The terminal device 901 includes: an imaging device, a network communication module, and a display device. The imaging device is used for acquiring an image to be identified; the network communication module is configured to send the image to be recognized to the server 902, and after receiving the image to be recognized, the server 902 determines a target object in the image to be recognized in response to receiving a recognition operation for the image to be recognized, then obtains a recognition result corresponding to the target object, and outputs the recognition result to the terminal device 901. The network communication module of the terminal device 901 may receive the recognition result output by the server 902 and corresponding to the target object in the image to be recognized; and the display device is used for displaying the image to be identified and the identification result.

Further, the terminal device 901 may further include: sounding device, power supply structure and protective structure. The voice device is used for broadcasting the recognition result in a voice mode; the power supply structure is used for supplying power to the terminal device 901; the protective structure user protects the terminal device 901.

It should be noted that the terminal device 901 is shaped like a magnifying glass, and the lens is a rectangular or circular electronic display, where the electronic display supports a touch function. Meanwhile, the terminal device 901 has a handle, which is convenient for a user to hold the device, and the handle may have one or more physical buttons for operating the device (or may have no buttons, and the device is operated by using a touch electronic display screen).

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. An image recognition method, comprising:

acquiring an image to be identified;

in response to receiving a recognition operation for the image to be recognized, determining a target object in the image to be recognized;

and acquiring and outputting the identification result corresponding to the target object.

2. The method of claim 1, wherein the determining a target object in the image to be identified in response to receiving an identification operation for the image to be identified comprises:

in response to receiving a whole recognition operation for the image to be recognized, acquiring position information of each object in the image to be recognized;

determining the target object based on the location information.

3. The method of claim 1 or 2, wherein the obtaining and outputting the recognition result corresponding to the target object comprises:

acquiring attribute information of a user;

determining a content information base matched with the user based on the attribute information;

acquiring content information corresponding to the target object from the content information base as the identification result;

and outputting the identification result.

4. The method of claim 1 or 2, wherein the obtaining and outputting the recognition result corresponding to the target object comprises:

responding to the target object as an animate object, and acquiring growth state information corresponding to the target object as the identification result;

and outputting the identification result based on the time sequence.

5. The method of claim 4, wherein the obtaining and outputting a recognition result corresponding to the target object further comprises:

determining category information of the target object in response to the target object being an animal class;

and acquiring and outputting the characteristic information of the object corresponding to the category information.

6. The method of claim 1, wherein the method further comprises:

acquiring current geographical position information;

generating recommendation information based on the current geographic position information and the target object;

and outputting the recommendation information.

7. An image recognition apparatus comprising:

a first acquisition module configured to acquire an image to be recognized;

a determination module configured to determine a target object in the image to be recognized in response to receiving a recognition operation for the image to be recognized;

a first output module configured to acquire and output a recognition result corresponding to the target object.

8. The apparatus of claim 7, wherein the means for determining comprises:

a first obtaining sub-module configured to obtain position information of each object in the image to be recognized in response to receiving a whole recognition operation for the image to be recognized;

a first determination submodule configured to determine the target object based on the position information.

9. The apparatus of claim 7 or 8, wherein the first output module comprises:

a second obtaining sub-module configured to obtain attribute information of the user;

a second determination sub-module configured to determine a content information base matching the user based on the attribute information;

a third obtaining sub-module configured to obtain content information corresponding to the target object from the content information base as the recognition result;

a first output submodule configured to output the recognition result.

10. The apparatus of claim 7 or 8, wherein the first output module comprises:

a fourth obtaining sub-module configured to, in response to the target object being an animate object, obtain growth state information corresponding to the target object as the identification result;

a second output submodule configured to output the recognition result based on a time sequence.

11. The apparatus of claim 10, wherein the first output module further comprises:

a third determining sub-module configured to determine category information of the target object in response to the target object being an animal class;

and the third output submodule is configured to acquire and output the characteristic information of the object corresponding to the category information.

12. The apparatus of claim 7, wherein the apparatus further comprises:

a second obtaining module configured to obtain current geographical location information;

a generation module configured to generate recommendation information based on the current geographic location information and the target object;

a second output module configured to output the recommendation information.

13. A server, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.

16. An image recognition system comprising: a terminal device and a server according to claim 13;

the terminal device includes:

the imaging device is used for acquiring an image to be identified;

the network communication module is used for sending the image to be recognized to the server and receiving a recognition result which is output by the server and corresponds to a target object in the image to be recognized;

and the display device is used for displaying the image to be identified and the identification result.