CN109376268A - Video classification methods, device, electronic equipment and computer readable storage medium - Google Patents
Video classification methods, device, electronic equipment and computer readable storage medium Download PDFInfo
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Abstract
Present disclose provides a kind of video classification methods, device, electronic equipment and computer readable storage mediums.This method comprises: extracting the frame picture of predetermined quantity in video；Type belonging to each frame picture is determined by picture classification model；According to each type determined, determine that video is belonging respectively to the probability of each type, the type that probability is greater than or equal to first threshold is determined as to the type of video, to realize the intelligent classification of each video, it is quickly searched convenient for user according to sorted video, the efficiency that user selects video is improved, user experience is enhanced.
Description
Technical field
This disclosure relates to technical field of video processing, specifically, this disclosure relates to a kind of video classification methods, device,
Electronic equipment and computer readable storage medium.
Background technique
With the development of network technology and popularizing for intelligent mobile terminal, everybody can shoot conveniently video to record at one's side
Strange thing, everybody is also happy to share the video of shooting with more people by mobile terminal, such as selects from photograph album
It selects a video and is uploaded to Online Video website, short video class application program or major social platform etc..
At this time, if the video in photograph album is excessive, user searches target video can be relatively difficult, especially when user needs
When uploading multiple same type of videos, the searching work of video will spend user's a large amount of time, seriously affect user's
Share experience.
Summary of the invention
To overcome above-mentioned technical problem or at least being partially solved above-mentioned technical problem, spy proposes following technical scheme:
In a first aspect, present disclose provides a kind of video classification methods, this method comprises:
The frame picture of predetermined quantity is extracted in video；
Type belonging to each frame picture is determined by picture classification model；
According to each type determined, determines that video is belonging respectively to the probability of each type, probability is greater than or equal to
The type of first threshold is determined as the type of video.
In an optional implementation manner, picture classification model is the machine learning model of pre-training.
In an optional implementation manner, if the probability that video is belonging respectively to each type is respectively less than first threshold,
The type for determining video is unidentified type.
In an optional implementation manner, according to each type determined, determine that video is belonging respectively to each type
Probability before, this method further include:
If it is determined that each type type be greater than or equal to second threshold, it is determined that the type of video be unidentified class
Type.
In an optional implementation manner, the type for determining video is this method after unidentified type further include:
User is obtained for the customization type of the video input of unidentified type, customization type is determined as video
Type.
In an optional implementation manner, after the type for determining video, this method further include:
The type of video is stored.
Second aspect, present disclose provides a kind of visual classification device, which includes:
Extraction module, for extracting the frame picture of predetermined quantity in video；
Frame picture type determining module, for determining type belonging to each frame picture by picture classification model；
Video type determining module, for determining that video is belonging respectively to each type according to each type determined
The type that probability is greater than or equal to first threshold is determined as the type of video by probability.
In an optional implementation manner, picture classification model is the machine learning model of pre-training.
In an optional implementation manner, if video type determining module is also used to video and is belonging respectively to each type
Probability is respectively less than first threshold, it is determined that the type of video is unidentified type.
In an optional implementation manner, video type determining module be also used to if it is determined that each type type
More than or equal to second threshold, it is determined that the type of video is unidentified type.
In an optional implementation manner, video type determining module is also used to obtain user for unidentified type
Customization type is determined as the type of video by the customization type of video input.
In an optional implementation manner, video type determining module is also used to store the type of video.
The third aspect, present disclose provides a kind of electronic equipment, which includes:
Processor and memory, memory are stored at least one instruction, at least one section of program, code set or instruction set,
At least one instruction, at least one section of program, code set or instruction set loads by processor and are executed the to realize such as disclosure
Method shown in any optional implementation of one side or first aspect.
Fourth aspect, present disclose provides a kind of computer readable storage mediums, and computer storage medium is based on storing
The instruction of calculation machine, program, code set or instruction set, when run on a computer, so that computer executes the such as the disclosure
Method shown in any optional implementation of one side or first aspect.
Technical solution provided by the present disclosure has the benefit that video classification methods, the device, electricity that the disclosure provides
Sub- equipment and computer readable storage medium, using the frame picture for extracting predetermined quantity in video；Pass through picture classification model
Determine type belonging to each frame picture；According to each type determined, determine that video is belonging respectively to the probability of each type,
The type that probability is greater than or equal to first threshold is determined as the type of video, to realize the intelligent classification of each video, just
It is quickly searched in user according to sorted video, improves the efficiency that user selects video, enhance user experience.
Detailed description of the invention
It, below will be to institute in embodiment of the present disclosure description in order to illustrate more clearly of the technical solution in the embodiment of the present disclosure
Attached drawing to be used is needed to be briefly described.
Fig. 1 is a kind of flow diagram for video classification methods that the embodiment of the present disclosure provides；
Fig. 2 is the exemplary diagram that the visual classification that the embodiment of the present disclosure provides is shown；
Fig. 3 is a kind of structural schematic diagram for visual classification device that the embodiment of the present disclosure provides；
Fig. 4 is the structural schematic diagram for a kind of electronic equipment that the embodiment of the present disclosure provides.
Specific embodiment
Embodiment of the disclosure is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and is only used for explaining the disclosure, and cannot be construed to the limitation to the disclosure.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singular " one " used herein, " one
It is a ", " described " and "the" may also comprise plural form.It is to be further understood that being arranged used in the specification of the disclosure
Diction " comprising " refer to that there are the feature, integer, step, operation, element and/or component, but it is not excluded that in the presence of or addition
Other one or more features, integer, step, operation, element, component and/or their group.It should be understood that when we claim member
Part is " connected " or when " coupled " to another element, it can be directly connected or coupled to other elements, or there may also be
Intermediary element.In addition, " connection " used herein or " coupling " may include being wirelessly connected or wirelessly coupling.It is used herein to arrange
Diction "and/or" includes one or more associated wholes for listing item or any cell and all combinations.
To keep the purposes, technical schemes and advantages of the disclosure clearer, below in conjunction with attached drawing to disclosure embodiment party
Formula is described in further detail.These specific embodiments can be combined with each other below, for the same or similar concept
Or process may repeat no more in certain embodiments.
The embodiment of the present disclosure provides music choosing method when a kind of shooting video, as shown in Figure 1, this method comprises:
Step S101: the frame picture of predetermined quantity is extracted in video；
Step S102: type belonging to each frame picture is determined by picture classification model；
Step S103: according to each type determined, determining that video is belonging respectively to the probability of each type, and probability is big
It is determined as the type of video in or equal to the type of first threshold.
Based on the video classification methods that the embodiment of the present disclosure provides, the intelligent classification of each video can be realized, convenient for using
Family quickly searches target video according to sorted video and executes subsequent sharing operation, improves the efficiency that user selects video,
Enhance user experience.
It is appreciated that in the embodiment of the present disclosure, each of file or photograph album for storage video video,
The video classification methods that the embodiment of the present disclosure provides are executed, so that when user's opened file folder or photograph album, as shown in Fig. 2, can
To show sorted video.
Wherein, opened file folder or photograph album can be user and click directly on file or photograph album come video of reading.It can also be with
Being user, (or website, platform etc., the embodiment of the present disclosure is introduced by taking software as an example, hereinafter phase in video sharing software
With place repeat no more) uploaded videos when, opened file folder or photograph album select video in video sharing software indirectly.
Specifically, being directed to each video to be sorted, in step S101 first, predetermined quantity is extracted in video
Frame picture.The frame picture of the former frames of video is e.g. extracted, or former frames can also be extracted plus intermediate frame etc..In another example
It is to extract the frame picture of anchor-frame, or the frame picture etc. of specific position is extracted according to the duration of video.Those skilled in the art
Member can select suitable extracting mode according to the actual situation, can also carry out according to demand to the predetermined quantity for extracting frame picture
Setting, such as 5 frames, 10 frames, 20 frames or 20 frames, with first-class, the embodiment of the present disclosure is not construed as limiting this.
Then, in step s 102, type belonging to each frame picture is determined by picture classification model.
Wherein, picture classification model is the machine learning model of pre-training.
In the embodiment of the present disclosure, the picture classification model can be trained in advance.Specifically, determining each in sample set
Type belonging to samples pictures, by each samples pictures and its corresponding type come repetitive exercise picture classification model.
Then, it by the picture classification model after each frame picture extracted in step S101 input training, exports belonging to each frame picture
Type.
Alternatively, the picture classification model can be trained by server end, the video that the embodiment of the present disclosure provides is executed
When classification method, the picture classification model after training, then, each frame figure that will be extracted in step S101 are obtained from server end
Picture classification model after piece input training, exports type belonging to each frame picture.
Alternatively, the picture classification model is trained by server end, the visual classification that the embodiment of the present disclosure provides is executed
When method, each frame picture extracted in step S101 is sent to server end, passes through the picture classification model of server end
It determines type belonging to each frame picture, type belonging to each frame picture that server end returns is received, to execute step
S103。
In a kind of feasible implementation, if the video classification methods that the embodiment of the present disclosure is provided are applied to iOS system
In software or platform, the picture classification model that can also directly adopt iOS system offer determines class belonging to each frame picture
Type.
Then, in step s 103, according to each type determined, determine that video is belonging respectively to the general of each type
The type that probability is greater than or equal to first threshold is determined as the type of video by rate.
Specifically, determining that the mode for the probability that video is belonging respectively to each type comprises determining that each type is corresponding
The quantity of frame picture accounts for the ratio (such as percentage) of the frame picture number extracted in video, belongs to each class respectively as video
The probability of type.
As exemplary, the quantity of the frame picture extracted in certain video is five frames, wherein first frame passes through figure to the 4th frame
Piece disaggregated model is determined as type of tour, and the 5th frame is determined as dancing type by picture classification model, then the video belongs to trip
The probability for swimming type is 80%, and the probability for belonging to dancing type is 20%.
In the embodiment of the present disclosure, (hereinafter, the type that probability is greater than or equal to first threshold is determined as the type of video
Such case is known as identification types).In practical application, first threshold is higher, and the accuracy of visual classification is higher.But consider
To the presence of noise, to realize reasonable classifying quality, those skilled in the art can according to the actual situation to first threshold into
Row setting, the embodiment of the present disclosure is it is not limited here.
Example is connected, if setting 80% for first threshold, since the probability that above-mentioned video belongs to type of tour is 80%, etc.
In first threshold, then it can determine that the type of the video is type of tour.
And so on, for each video to be sorted, classification can be realized by the above process.The embodiment of the present disclosure
In, as shown in Fig. 2, can be shown respectively sorted directly under each tag along sort in user's opened file folder or photograph album
Video.Wherein, the tag along sort of display can be determined according to the type of each video identified.
Example is connected again, if setting 90% for first threshold, then the probability that video belongs to type of tour is 80%, is less than
First threshold, and the probability for belonging to dancing type is 20%, again smaller than first threshold.In the embodiment of the present disclosure, if video is distinguished
The probability for belonging to each type is respectively less than first threshold, it is determined that the type of video is unidentified type.In i.e. upper example, first
In the case that threshold value is set as 90%, which can be classified as unidentified type.
In practical application, if type belonging to each frame picture identified in step S102 by picture classification model compared with
More, the probability that the video usually determined is belonging respectively to each type is difficult to be more than first threshold.As illustratively, in certain video
The quantity of the frame picture of extraction is five frames, wherein first frame and the 4th frame are determined as type of tour by picture classification model, the
Two frames and third frame are determined as type of play by picture classification model, and the 5th frame is determined as dancing class by picture classification model
Type, it is possible to which the probability for determining that the video belongs to type of tour is 40%, the probability for belonging to type of play is 40%, is belonged to
In dancing type probability be 20%.Since the probability difference that video belongs to each type is smaller, it is difficult to determine which video belongs to
Seed type.
Based on this, the embodiment of the present disclosure be promoted visual classification efficiency, reduce calculation amount, can before step S102,
If it is determined that each type type be greater than or equal to second threshold, it is determined that the type of video be unidentified type.
The type of video is determined by the type of video type can no longer to execute step after unidentified type
S103 reduces calculation amount to promote visual classification efficiency.
In practical application, those skilled in the art can be according to the actual situation configured second threshold, such as in conjunction with
The predetermined quantity of frame picture is extracted in video and above-mentioned first threshold is configured second threshold, and the embodiment of the present disclosure is herein not
It limits.
In the embodiment of the present disclosure, for being determined as the video of unidentified type, unidentified type can be directly displayed at
Under tag along sort, wherein for the video under the tag along sort of unidentified type, the embodiment of the present disclosure can provide type and make by oneself
The function of justice.
Specifically, after the type for determining video is unidentified type, the visual classification of embodiment of the present disclosure offer
Method further include: obtain user for the customization type of the video input of unidentified type, customization type is determined as regarding
The type of frequency.
That is, user can take customized mode to classify.Specifically, user can be directed to it is unidentified
Video (for convenience of describing, the video is hereafter known as target video) input customization type of type.Wherein, if user from
Define type and above-mentioned identified any video type (such as type of tour etc., for convenience of describing, hereafter by any video
Type is known as the first video type) it is identical, then the first video type is determined as to the type of target video.The target can be regarded
Frequency, which is moved under the corresponding tag along sort of the first video type, to be shown.Alternatively, if the customization type of user with identified
Video type be all different, then increase video type newly and (for convenience of describing, be hereafter known as the newly-increased customization type
Second video type) and its corresponding tag along sort, it can be under the corresponding tag along sort of the second video type, displaying target view
Frequently.Optionally, after the video of unidentified type is customized by user, the corresponding contingency table of unidentified type can be deleted
Label.
In the embodiment of the present disclosure, the type of video can also be stored after determining the type of video.
Specifically, each video is carried out with the corresponding type identified for each video of identification types
Mapping storage；For each video of unidentified type, if having obtained the customization type of user, by each video with it is corresponding
Customization type carries out mapping storage.Each video of the unidentified type of customization type non-for user, by its with do not know
Other type carries out mapping storage.
For the embodiment of the present disclosure, the type of video is stored, in next opened file folder or photograph album, need pair
The video being newly added is classified, the type for having divided the video of class to directly read storage.
So, in a kind of feasible implementation, before step S101, it is thus necessary to determine that video to be sorted.It is specific and
Speech, determines whether the type of video is stored, if the type of video is not stored, it is determined that be video to be sorted.
Conversely, the type of storage to be determined as to the type of video if the type of video has stored, directly read and is carried out
Display, it is not necessary to execute subsequent step S101~step S103 again, effectively reduce sorting algorithm operand, promote visual classification
Efficiency.
The video classification methods that the embodiment of the present disclosure provides, using the frame picture for extracting predetermined quantity in video；Pass through
Picture classification model determines type belonging to each frame picture；According to each type determined, determine that video is belonging respectively to respectively
The type that probability is greater than or equal to first threshold is determined as the type of video, to realize each video by the probability of a type
Intelligent classification, quickly searched convenient for user according to sorted video, improve user select video efficiency, enhancing use
Family experience.
The embodiment of the present disclosure additionally provides a kind of visual classification device, as shown in figure 3, the visual classification device 30 can wrap
It includes: extraction module 301, frame picture type determining module 302 and video type determining module 303, wherein
Extraction module 301 for extracting the frame picture of predetermined quantity in video；
Frame picture type determining module 302 is used to determine type belonging to each frame picture by picture classification model；
Video type determining module 303 is used to determine that video is belonging respectively to each type according to each type determined
Probability, the type that probability is greater than or equal to first threshold is determined as the type of video.
In an optional implementation manner, picture classification model is the machine learning model of pre-training.
In an optional implementation manner, if video type determining module 303 is also used to video and is belonging respectively to each class
The probability of type is respectively less than first threshold, it is determined that the type of video is unidentified type.
In an optional implementation manner, video type determining module 303 be also used to if it is determined that each type
Type is greater than or equal to second threshold, it is determined that the type of video is unidentified type.
In an optional implementation manner, video type determining module 303 is also used to obtain user for unidentified class
Customization type is determined as the type of video by the customization type of the video input of type.
In an optional implementation manner, video type determining module 303 is also used to store the type of video.
Visual classification device provided by the embodiment of the present disclosure for the specific hardware in equipment or can be installed on equipment
On software or firmware etc., the technical effect of realization principle and generation is identical with preceding method embodiment, to briefly describe, if
Standby embodiment part does not refer to place, can refer to corresponding contents in preceding method embodiment, details are not described herein.
The visual classification device that the embodiment of the present disclosure provides, using the frame picture for extracting predetermined quantity in video；Pass through
Picture classification model determines type belonging to each frame picture；According to each type determined, determine that video is belonging respectively to respectively
The type that probability is greater than or equal to first threshold is determined as the type of video, to realize each video by the probability of a type
Intelligent classification, quickly searched convenient for user according to sorted video, improve user select video efficiency, enhancing use
Family experience.
Based on principle identical with the method for list is updated in the embodiment of the present disclosure, the embodiment of the present disclosure additionally provides one kind
Electronic equipment, the electronic equipment include memory and processor, memory be stored at least one instruction, at least one section of program,
Code set or instruction set, at least one instruction, at least one section of program, code set or instruction set are loaded by processor and execute this public affairs
Open method shown in any of the above-described embodiment.
Based on principle identical with the method for list is updated in the embodiment of the present disclosure, one is additionally provided in the embodiment of the present disclosure
Kind computer readable storage medium, computer storage medium are used to store computer instruction, program, code set or instruction set, when
When it runs on computers, so that computer executes method shown in any of the above-described embodiment of the disclosure.
Below with reference to Fig. 4, it illustrates the structural schematic diagrams for the electronic equipment 40 for being suitable for being used to realize the embodiment of the present disclosure.
Electronic equipment in the embodiment of the present disclosure can include but is not limited to such as mobile phone, laptop, Digital Broadcasting Receiver
Device, PDA (personal digital assistant), PAD (tablet computer), PMP (portable media player), car-mounted terminal are (such as vehicle-mounted
Navigation terminal) etc. mobile terminal and such as number TV, desktop computer etc. fixed terminal.Electronics shown in Fig. 4
Equipment is only an example, should not function to the embodiment of the present disclosure and use scope bring any restrictions.
As shown in figure 4, electronic equipment 40 may include processing unit (such as central processing unit, graphics processor etc.) 401,
It can be loaded into random access storage according to the program being stored in read-only memory (ROM) 402 or from storage device 408
Program in device (RAM) 403 and execute various movements appropriate and processing.In RAM 403, it is also stored with the behaviour of electronic equipment 40
Various programs and data needed for making.Processing unit 401, ROM402 and RAM 403 are connected with each other by bus 404.Input/
Output (I/O) interface 405 is also connected to bus 404.
In general, following device can connect to I/O interface 405: including such as touch screen, touch tablet, keyboard, mouse, taking the photograph
As the input unit 406 of head, microphone, accelerometer, gyroscope etc.；Including such as liquid crystal display (LCD), loudspeaker, vibration
The output device 407 of dynamic device etc.；Storage device 408 including such as tape, hard disk etc.；And communication device 409.Communication device
409, which can permit electronic equipment 40, is wirelessly or non-wirelessly communicated with other equipment to exchange data.Although Fig. 4, which is shown, to be had
The electronic equipment 40 of various devices, it should be understood that being not required for implementing or having all devices shown.It can substitute
Implement or have more or fewer devices in ground.
Particularly, in accordance with an embodiment of the present disclosure, it may be implemented as computer above with reference to the process of flow chart description
Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising be carried on computer-readable medium
On computer program, which includes the program code for method shown in execution flow chart.In such reality
It applies in example, which can be downloaded and installed from network by communication device 409, or from storage device 408
It is mounted, or is mounted from ROM 402.When the computer program is executed by processing unit 401, the embodiment of the present disclosure is executed
Method in the above-mentioned function that limits.
It should be noted that the above-mentioned computer-readable medium of the disclosure can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but not
Be limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.Meter
The more specific example of calculation machine readable storage medium storing program for executing can include but is not limited to: have the electrical connection, just of one or more conducting wires
Taking formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed read-only storage
Device (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In the disclosure, computer readable storage medium can be it is any include or storage journey
The tangible medium of sequence, the program can be commanded execution system, device or device use or in connection.And at this
In open, computer-readable signal media may include in a base band or as the data-signal that carrier wave a part is propagated,
In carry computer-readable program code.The data-signal of this propagation can take various forms, including but not limited to
Electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be computer-readable and deposit
Any computer-readable medium other than storage media, the computer-readable signal media can send, propagate or transmit and be used for
By the use of instruction execution system, device or device or program in connection.Include on computer-readable medium
Program code can transmit with any suitable medium, including but not limited to: electric wire, optical cable, RF (radio frequency) etc. are above-mentioned
Any appropriate combination.
Above-mentioned computer-readable medium can be included in above-mentioned electronic equipment；It is also possible to individualism, and not
It is fitted into the electronic equipment.
Above-mentioned computer-readable medium carries one or more program, when said one or multiple programs are by the electricity
When sub- equipment executes, so that the electronic equipment: extracting the frame picture of predetermined quantity in video；It is determined by picture classification model
Type belonging to each frame picture；According to each type determined, determine that video is belonging respectively to the probability of each type, it will be general
The type that rate is greater than or equal to first threshold is determined as the type of video.
The calculating of the operation for executing the disclosure can be write with one or more programming languages or combinations thereof
Machine program code, above procedure design language include object oriented program language-such as Java, Smalltalk, C+
+, it further include conventional procedural programming language-such as " C " language or similar programming language.Program code can
Fully to execute, partly execute on the user computer on the user computer, be executed as an independent software package,
Part executes on the remote computer or executes on a remote computer or server completely on the user computer for part.
In situations involving remote computers, remote computer can pass through the network of any kind --- including local area network (LAN)
Or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as utilize Internet service
Provider is connected by internet).
Flow chart and block diagram in attached drawing are illustrated according to the system of the various embodiments of the disclosure, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of the module, program segment or code include one or more use
The executable instruction of the logic function as defined in realizing.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are actually
It can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it to infuse
Meaning, the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart can be with holding
The dedicated hardware based system of functions or operations as defined in row is realized, or can use specialized hardware and computer instruction
Combination realize.
Being described in unit involved in the embodiment of the present disclosure can be realized by way of software, can also be by hard
The mode of part is realized.Wherein, the title of unit does not constitute the restriction to the unit itself under certain conditions, for example, the
One acquiring unit is also described as " obtaining the unit of at least two internet protocol addresses ".
Above description is only the preferred embodiment of the disclosure and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that the open scope involved in the disclosure, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from design disclosed above, it is carried out by above-mentioned technical characteristic or its equivalent feature
Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed in the disclosure
Can technical characteristic replaced mutually and the technical solution that is formed.
It should be understood that although each step in the flow chart of attached drawing is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, can execute in the other order.Moreover, at least one in the flow chart of attached drawing
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, execution sequence, which is also not necessarily, successively to be carried out, but can be with other
At least part of the sub-step or stage of step or other steps executes in turn or alternately.
The above is only some embodiments of the disclosure, it is noted that for the ordinary skill people of the art
For member, under the premise of not departing from disclosure principle, several improvements and modifications can also be made, these improvements and modifications are also answered
It is considered as the protection scope of the disclosure.
Claims (10)
1. a kind of video classification methods, which is characterized in that the described method includes:
The frame picture of predetermined quantity is extracted in video；
Type belonging to each frame picture is determined by picture classification model；
According to each type determined, determine that the video is belonging respectively to the probability of each type, probability is greater than or
Equal to the type that the type of first threshold is determined as the video.
2. video classification methods according to claim 1, which is characterized in that the picture classification model is the machine of pre-training
Device learning model.
3. video classification methods according to claim 1, which is characterized in that if the video is belonging respectively to each class
The probability of type is respectively less than first threshold, it is determined that the type of the video is unidentified type.
4. video classification methods according to claim 1, which is characterized in that according to each type determined, determine institute
Video is stated to be belonging respectively to before the probability of each type, the method also includes:
If it is determined that each type type be greater than or equal to second threshold, it is determined that the type of the video be unidentified class
Type.
5. video classification methods according to claim 3 or 4, which is characterized in that the type for determining the video is not know
After other type, the method also includes:
User is obtained for the customization type of the video input of the unidentified type, the customization type is determined as institute
State the type of video.
6. video classification methods according to claim 5, which is characterized in that described after the type for determining the video
Method further include:
The type of the video is stored.
7. a kind of visual classification device, which is characterized in that described device includes:
Extraction module, for extracting the frame picture of predetermined quantity in video；
Frame picture type determining module, for determining type belonging to each frame picture by picture classification model；
Video type determining module, for determining that the video is belonging respectively to each class according to each type determined
The type that probability is greater than or equal to first threshold is determined as the type of the video by the probability of type.
8. visual classification device according to claim 7, which is characterized in that if the video type determining module is also used to
The probability that the video is belonging respectively to each type is respectively less than first threshold, it is determined that the type of the video is unidentified
Type.
9. a kind of electronic equipment characterized by comprising
Processor and memory, the memory are stored at least one instruction, at least one section of program, code set or instruction set,
It is described at least one instruction, at least one section of program, the code set or the instruction set loaded by the processor and executed with
Realize as the method according to claim 1 to 6.
10. a kind of computer readable storage medium, which is characterized in that the computer storage medium refers to for storing computer
It enables, program, code set or instruction set, when run on a computer, so that computer is executed such as any one of claim 1-6
The method.
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| CN201811427554.4A CN109376268A (en) | 2018-11-27 | 2018-11-27 | Video classification methods, device, electronic equipment and computer readable storage medium |
| PCT/CN2018/125409 WO2020107625A1 (en) | 2018-11-27 | 2018-12-29 | Video classification method and apparatus, electronic device, and computer readable storage medium |
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| CN201811427554.4A CN109376268A (en) | 2018-11-27 | 2018-11-27 | Video classification methods, device, electronic equipment and computer readable storage medium |
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