CN1851709A - Embedded multimedia content-based inquiry and search realizing method - Google Patents
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- 230000006978 adaptation Effects 0.000 claims description 3
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Abstract
The present invented method includes object labeling, feature extraction to multimedia data, establishing index using extractive media features, inputting user description, user description similar matching with library media, selecting final result in similar matched result. The present invention breaches traditional text retrieval technologic limitation, directly analysing picture, video, audio content, extracting features, utilizes these content features establishing index and searching, to make searching more approaching media object.
Description
Technical field
The present invention relates to the built-in multimedia technical field, particularly relate to the implementation method of content-based inquiry of a kind of built-in multimedia and retrieval.
Background technology
Along with the rapid development of multimedia nineties in 20th century, and the continuous appearance of new efficient multimedia coding techniques, multimedia messagess such as a large amount of videos, audio frequency and image will become online indispensable valuable source, especially the application of video data in life more and more widely, and every day all producing a large amount of audiovisual informations, cause storage, management and utilize the very difficulty that becomes again these multimedia documents.The development of multimedia technology and Internet brings huge multimedia messages ocean to people, and further caused the generation of ultra-large type multimedia information lib, light is what to be difficult to accomplish to the description of multimedia messages and retrieval with keyword, and this just needs a kind of at multimedia effective retrieval mode.How effectively to help people to find needed multimedia messages quickly and accurately, become multimedia information lib key problem to be solved, how removing the multi-medium data of management and retrieval magnanimity effectively according to the characteristic of multi-medium data, it is most important just to seem.
Traditional data type mainly is integer, full mold, Boolean type and character type, and its database technology can adopt the retrieval mode based on keyword.And in the multi-medium data processing, except the data type of above-mentioned routine, also to handle data types such as image, figure, audio frequency and video flowing.If being used for multimedia messages, this search method based on keyword will have any problem, because the same text of multimedia messages, numerical information are essentially different.
Multimedia messages has non-structured characteristic.In traditional database, recorded information has tangible structuring characteristic, and it is the reflection of mutual relationship between the object in the real world, can obtain by relational model is abstract.Multimedia messages has stronger destructuring characteristic, and all there be (such as video flowing, audio stream) in it with the form of stream.This category information needs complicated medium to cut apart and organizational technology if carry out structuring and handle.
Multimedia messages has the polysemy of content.In traditional database, the semantic information that each record is comprised is definite and limited, and same content has different explanations in the multimedia messages in different application, promptly has the characteristics of polysemy.In order to solve in the problem aspect the multimedia information retrieval, ISO has set up a MPEG-7 of working group and has specialized in the content description problem of multimedia messages, and expects to come the description of standard multimedia messages content by formulating relevant international standard.
MPEG-7 describes the standardization of dissimilar multimedia messagess, and this description is only relevant with the content of multimedia messages itself, and purpose of description is to use the information that the family searches oneself quickly and efficiently to be needed.The formal title of MPEG-7 is description interface (the Multimedia ContentDescription Inter-face) o of content of multimedia.
MPEG-7 be based upon information cut apart with feature extraction on, that is to say that it only is described information characteristics, and and be indifferent to these features and how obtain.MPEG-7 also links to each other with search engine simultaneously, and the content that search engine can utilize MPEG-7 to describe is searched for and return results is given the user, and MPEG-7 itself does not participate in the search procedure of information directly.Like this, though the methods of feature extraction are a lot, the implementation of search engine is also different, but MPEG-7 is with the interface that standard is provided at them, so search bow I holds up the details that can be concerned about the realization feature extraction and only needs carry out information search with regard to the information description of standard, so MPEG-7 plays outstanding bridge in content-based retrieval.
MPEG-7 will utilize the feature that extracts from medium when describing the content of multimedia messages.The description of feature is finished by-serial descriptor D (Descriptor) in MPEG-7.Mutual relationship between the descriptor is come standard by description scheme DS (Description Scheme).Meanwhile, MPEG-7 also will formulate a kind of Description Definition Language DDL (Description definition Language) specification description scheme.
Because the content of multimedia messagess such as image, video has abundant intension, and the Multimedia Content Description Interface MPEG-7 of ISO definition will formally become international standard.So information retrieval based on contents (Co ntentBased Image Retrieval, just arise at the historic moment by technology CBIR).
Content-based retrieval is a kind of novel multimedia retrieval technology.It is meant according to the content of medium and media object and contextual relation and retrieves in the large scale multimedia database, mainly is that semanteme, vision and the aural signature that utilizes media object retrieved.It has broken through traditional limitation based on the text retrieval technology, directly feature is analyzed, extracted to image, video, audio content, utilizes these content characteristics to set up the index line retrieval of going forward side by side, and makes retrieval more near media object.As utilize color, texture, shape in the image, the motion of the camera lens in the video, scene, camera lens, the tone in the sound, loudness, tone color etc.Its goal in research provides at the algorithm that does not have can discern automatically or understand under the human situation about participating in the image key character.This shows that content-based retrieval is a cross discipline that involvement aspect is very wide, need utilize technology such as Flame Image Process, pattern-recognition, computer vision, image understanding, be the synthetic of multiple technologies, thereby have a wide range of applications.
Because the display mode of embedded mobile terminal varies, arithmetic capability also has bigger gap, in general, the arithmetic speed of its CPU from several MHz to hundreds of MHz, the user is for retrieval rate, the requirement difference that video shows, in addition, because the restriction of the network bandwidth, the video of embedded mobile terminal shows the influence that will be subjected to bandwidth, therefore, with respect to the implementation method of general Content-based Video Retrieval, be applied to the implementation method of built-in multimedia Content-based Video Retrieval, it is to the semanteme of media object, the extraction of vision and aural signature has its different characteristics with retrieval.For built-in multimedia, the extraction of content need be according to the requirement of different embedded mobile terminals, the feature extraction of classification property, such as for the demanding terminal of response speed, its Feature Extraction will be simplified, and be top priority to satisfy real-time, and for the terminal that requires accurately to mate faster, its Feature Extraction will comprehensive and abundant, so that can find the media fragment of request fast.
The object of the present invention is to provide the implementation method of a kind of built-in multimedia based on the video frequency searching of key frame.
The technical scheme that the present invention solves its technical matters employing may further comprise the steps:
(1) object identity:
Use the video object segmentation program that the video data of depositing in server end is carried out video analysis and cuts apart, identify the static object of still image, video lens representative frame or the dynamic object in the video sequence;
(2) multi-medium data is carried out feature extraction:
The low-level image feature that the static object or the dynamic object in the video sequence of still image, video lens representative frame carried out color, texture and shape extracts to be handled;
(3) set up index with the media characteristic that extracts:
Select the different embedded device terminal of a plurality of adaptations characteristics, comprise CPU speed, the feature set of response time requirement and utilize new character representation method to set up index;
(4) the input user describes:
In the embedded device terminal, the user inquires about by the figure that the example browsing selective system and provide or user draw voluntarily, transmits query requests and data to server, by continuous modification example until finding the coupling target;
(5) user describe to the storehouse in the similar coupling of medium:
Medium carry out similar coupling according to matching algorithm in the query characteristics that server sends the embedded device terminal and the storehouse, hardware and customer requirements at the embedded device terminal, and current network bandwidth, taking-up is satisfied the record of threshold value as candidate result, by returning to the user after the big minispread of similarity;
(6) in the result of similar coupling, select net result:
The Query Result of user by browsing selection system to return at embedded mobile terminal perhaps selected an example from candidate result, through forming a new inquiry after the feature adjustment, finally obtain satisfied result by new inquiry.
The present invention compares with background technology, and the useful effect that has is:
Method of the present invention is based on the retrieval of content, directly multimedias such as text, image, video, audio frequency are analyzed, deposit multi-medium data in media library, therefrom extract content characteristic, the content characteristic that the feature and the pre-service of user's input are extracted is automatically put into feature database, utilize these content characteristics to set up the index line retrieval of going forward side by side then, these special and comprehensive knowledge then deposit knowledge base in.The present invention has broken through traditional limitation based on the text retrieval technology, directly feature is analyzed, extracted to image, video, audio content, utilizes these content characteristics to set up the index line retrieval of going forward side by side, and makes retrieval more near media object.
Description of drawings
Fig. 1 is an implementation process synoptic diagram of the present invention.
Embodiment
The present invention is the implementation method of content-based inquiry of a kind of built-in multimedia and retrieval, below in conjunction with Fig. 1 its specific implementation process is described.
1) object identity:
Content-based multimedia retrieval technological system generally allows the user in the mode of full-automatic or semi-automatic (needing user intervention) medium to be cut apart, identify the dynamic object in the user's interest zone (static object) and video sequence in the medium such as representative frame of still image, video lens, so that targetedly target is carried out feature extraction, description and inquiry.
2) multi-medium data is carried out feature extraction:
The object that user or system are indicated carries out feature extraction to be handled.Feature extraction can be of overall importance, as at entire image and video lens, also can be at certain object, and as the subregion in the image, motion object etc. in the video.
Feature extraction can be extracted features such as color, texture, shape for image.And for video, because video is a dynamic image, so the primary image feature extraction then has static nature to extract and behavioral characteristics extracts two big classes, has the video of overlapping text also can extract semantic contents such as text, key word.
3) set up index with the media characteristic that extracts:
Because identical medium may mean different things to different people, therefore the index as multi-medium data is not enough with one or two feature only, should select the feature set of a plurality of adaptation varying environments and utilize new character representation method, as feature mathematical notation based on fractal or small echo.CBIR for example, it is according to the information such as spatial relationship of color, texture, shape and object (subimage in the image) that image comprised, and the eigenvector of setting up image is its index.
4) the input user describes:
Describing medium more exactly is one steps of key of inquiring about.When some features of be difficult to describing were inquired about, the user inquired about by example or the own graphing browsing selective system and provide, and then by constantly revising example, progressively refinement, inquiry repeatedly is until finding the coupling target.
5) user describe to the storehouse in the similar coupling of medium:
Medium in query characteristics and the storehouse are carried out similar coupling (carrying out the similarity coupling as utilizing the distance function between the characteristics of image) according to certain matching algorithm, taking-up is satisfied the record of threshold value as candidate result, by returning to the user after the big minispread of similarity, for the user, video retrieval method the most directly perceived and most convenient is exactly: the user submits a width of cloth query image to searching system, searching system is returned a set of shots in the video library to the user in order according to the similarity on the content then, as result for retrieval.
6) in the result of similar coupling, select net result:
In order further to improve the accuracy of retrieval, the feedback information of user to result for retrieval collected in conjunction with the relevant feedback technology by many systems.This seems more outstanding in content-based multimedia retrieval.Because content-based retrieval is a kind of similarity retrieval, in retrieving, adopt the way of progressively refinement, needing constantly to carry out alternately with in a retrieving with the user, to the Query Result that system returns, the user can select by browsing, and perhaps selects an example from candidate result, after the feature adjustment, form a new inquiry.Content-based retrieval has experienced a feature adjustment, the cyclic process of coupling again, and this point has significantly differently with accurate matching process in the routine data library searching.
Content-based retrieval is briefly retrieved according to the content of multimedia messages exactly.It comprises the information content and retrieval two aspect contents.The information content is relevant with the understanding of information, and such as image understanding, video understanding etc.: retrieval is not only relevant with the searching method that adopts, and also the judgment criterion with coupling has relation.Generally, content-based information retrieval at first will be cut apart media information, makes it become independent searching object, and then each media object is carried out feature extraction, and the set of feature has just constituted its content description.Next, just can from multimedia information lib, return one group of immediate object of content description that requires with retrieval as requested.
At last, it is also to be noted that what more than enumerate only is specific embodiments of the invention.Obviously, the invention is not restricted to above examples of implementation, many distortion can also be arranged.All distortion that those of ordinary skill in the art can directly derive or associate from content disclosed by the invention all should be thought protection scope of the present invention.
Claims (1)
1, the content-based inquiry of built-in multimedia and the implementation method of retrieval is characterized in that, may further comprise the steps:
(1) object identity:
Use the video object segmentation program that the video data of depositing in server end is carried out video analysis and cuts apart, identify the static object of still image, video lens representative frame or the dynamic object in the video sequence;
(2) multi-medium data is carried out feature extraction:
The low-level image feature that the static object or the dynamic object in the video sequence of still image, video lens representative frame carried out color, texture and shape extracts to be handled;
(3) set up index with the media characteristic that extracts:
Select the different embedded device terminal of a plurality of adaptations characteristics, comprise CPU speed, the feature set of response time requirement and utilize new character representation method to set up index;
(4) the input user describes:
In the embedded device terminal, the user inquires about by the figure that the example browsing selective system and provide or user draw voluntarily, transmits query requests and data to server, by continuous modification example until finding the coupling target;
(5) user describe to the storehouse in the similar coupling of medium:
Medium carry out similar coupling according to matching algorithm in the query characteristics that server sends the embedded device terminal and the storehouse, hardware and customer requirements at the embedded device terminal, and current network bandwidth, taking-up is satisfied the record of threshold value as candidate result, by returning to the user after the big minispread of similarity;
(6) in the result of similar coupling, select net result:
The Query Result of user by browsing selection system to return at embedded mobile terminal perhaps selected an example from candidate result, through forming a new inquiry after the feature adjustment, finally obtain satisfied result by new inquiry.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008101422A1 (en) * | 2007-01-31 | 2008-08-28 | Tencent Technology (Shenzhen) Company Limited | Image searching method and system |
| WO2009129658A1 (en) * | 2008-04-24 | 2009-10-29 | Lonsou (Beijing) Technologies Co., Ltd. | System and method for knowledge-based input in a browser |
| CN101599065A (en) * | 2008-06-05 | 2009-12-09 | 日电（中国）有限公司 | Relevant inquiring organization system and method |
| CN101827266A (en) * | 2010-04-01 | 2010-09-08 | 公安部第三研究所 | Network video server with video structural description function and method for implementing video analysis description by using same |
| CN101833584A (en) * | 2010-05-20 | 2010-09-15 | 无敌科技(西安)有限公司 | System and method for searching teaching video contents in embedded equipment |
| CN101853297A (en) * | 2010-05-28 | 2010-10-06 | 英华达(南昌)科技有限公司 | Method for fast obtaining expected image in electronic equipment |
| CN102496118A (en) * | 2011-11-30 | 2012-06-13 | 苏州奇可思信息科技有限公司 | Advertisement output management method based on online games |
| US8392484B2 (en) | 2009-03-26 | 2013-03-05 | Alibaba Group Holding Limited | Shape based picture search |
| WO2013044407A1 (en) * | 2011-09-27 | 2013-04-04 | Hewlett-Packard Development Company, L.P. | Retrieving visual media |
| CN103518187A (en) * | 2011-03-10 | 2014-01-15 | 特克斯特怀茨有限责任公司 | Method and system for information modeling and applications thereof |
| WO2014032244A1 (en) * | 2012-08-30 | 2014-03-06 | Microsoft Corporation | Feature-based candidate selection |
| CN103929669A (en) * | 2014-04-30 | 2014-07-16 | 成都理想境界科技有限公司 | Interactive video generator, player, generating method and playing method |
| CN104683760A (en) * | 2015-01-28 | 2015-06-03 | 安科智慧城市技术（中国）有限公司 | Video processing method and system |
| US9330341B2 (en) | 2012-01-17 | 2016-05-03 | Alibaba Group Holding Limited | Image index generation based on similarities of image features |
| CN107801093A (en) * | 2017-10-26 | 2018-03-13 | 深圳市量子视觉科技有限公司 | Video Rendering method, apparatus, computer equipment and readable storage medium storing program for executing |
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2006
- 2006-05-25 CN CN 200610051627 patent/CN1851709A/en active Pending
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| WO2008101422A1 (en) * | 2007-01-31 | 2008-08-28 | Tencent Technology (Shenzhen) Company Limited | Image searching method and system |
| WO2009129658A1 (en) * | 2008-04-24 | 2009-10-29 | Lonsou (Beijing) Technologies Co., Ltd. | System and method for knowledge-based input in a browser |
| CN101599065A (en) * | 2008-06-05 | 2009-12-09 | 日电（中国）有限公司 | Relevant inquiring organization system and method |
| US8392484B2 (en) | 2009-03-26 | 2013-03-05 | Alibaba Group Holding Limited | Shape based picture search |
| CN101827266A (en) * | 2010-04-01 | 2010-09-08 | 公安部第三研究所 | Network video server with video structural description function and method for implementing video analysis description by using same |
| CN101833584A (en) * | 2010-05-20 | 2010-09-15 | 无敌科技(西安)有限公司 | System and method for searching teaching video contents in embedded equipment |
| CN101853297A (en) * | 2010-05-28 | 2010-10-06 | 英华达(南昌)科技有限公司 | Method for fast obtaining expected image in electronic equipment |
| CN103518187B (en) * | 2011-03-10 | 2015-07-01 | 特克斯特怀茨有限责任公司 | Method and system for information modeling and applications thereof |
| CN103518187A (en) * | 2011-03-10 | 2014-01-15 | 特克斯特怀茨有限责任公司 | Method and system for information modeling and applications thereof |
| WO2013044407A1 (en) * | 2011-09-27 | 2013-04-04 | Hewlett-Packard Development Company, L.P. | Retrieving visual media |
| US9229958B2 (en) | 2011-09-27 | 2016-01-05 | Hewlett-Packard Development Company, L.P. | Retrieving visual media |
| CN102496118A (en) * | 2011-11-30 | 2012-06-13 | 苏州奇可思信息科技有限公司 | Advertisement output management method based on online games |
| US9330341B2 (en) | 2012-01-17 | 2016-05-03 | Alibaba Group Holding Limited | Image index generation based on similarities of image features |
| WO2014032244A1 (en) * | 2012-08-30 | 2014-03-06 | Microsoft Corporation | Feature-based candidate selection |
| CN103929669A (en) * | 2014-04-30 | 2014-07-16 | 成都理想境界科技有限公司 | Interactive video generator, player, generating method and playing method |
| CN103929669B (en) * | 2014-04-30 | 2018-01-05 | 成都理想境界科技有限公司 | Can interactive video maker, player and its generation method, player method |
| CN104683760A (en) * | 2015-01-28 | 2015-06-03 | 安科智慧城市技术（中国）有限公司 | Video processing method and system |
| CN107801093A (en) * | 2017-10-26 | 2018-03-13 | 深圳市量子视觉科技有限公司 | Video Rendering method, apparatus, computer equipment and readable storage medium storing program for executing |
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| CN110825891A (en) * | 2019-10-31 | 2020-02-21 | 北京小米移动软件有限公司 | Multimedia information identification method and device and storage medium |
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