CN101021855A - Video searching system based on content - Google Patents
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Abstract
This invention provides a search system based on video program content characters including a search dispatch server used in analyzing and dispatching the search requests put forward by users to search in terms of a certain strategy to grade, arrange and merge the search result to feed them back to the user, an element data search server searching the element data of the video program based on the requirement of the server, a caption search server searching the XML file storing program caption texts, a video search cluster used in searching the character data of the video key frame, a phone search cluster used in searching the phonetic information of video programs including spelling series and spelling patterns and a search interface facing to users receiving request of users in terms of signed message format and feeding back the search result.
Description
Technical field
The present invention relates to Content-based Video Retrieval (CBR) field.Comprise tissue, the storage of video frequency feature data, the index of high dimensional feature vector and retrieval, the technology in fields such as distributed search.
Background technology
An information retrieval system generally includes the searching database of a core, search dispatching server and group of server.External then the search and the interface of information typing are provided.As shown in Figure 1.
Wherein, the information typing is to rely on the artificial mode of keying in mostly.Promptly the provider by retrieval of content in the system is entered into database offering the information typing interface of the content information user, that be used for searching for by searching system.
The core database system then mainly is responsible for the information data of storage for the usefulness of user search.
The search dispatching server is responsible for receiving, resolving user's request, and its searching request is distributed to retrieval server, to carry out actual retrieval.After result for retrieval turned back to the search dispatching server, the search dispatching server will be handled return results, as ordering, merging, screening etc.After handling it is returned to the user.Finish once search.
Different with common information retrieval system, video searching system is comparatively complicated, and the module that comprises is also more relatively.
A video searching system is made up of several big modules such as video features analysis, characteristic storage, search dispatching and Content-based Video Retrieval.Wherein, the storage of characteristic, search dispatching and video frequency searching are the nucleus modules of this type of search engine.As shown in Figure 2.
Traditional video frequency search system will obtain video frequency program for information about by manual annotation, and leaves these information in supply inquiry after this in the database usefulness.That is to say that analysis module is actual to be an operational module of being finished by hand by the people.
This mode has significant limitation.Artificial note not only expends great amount of manpower and time, and often has very big subjectivity, can not make accurate, just portrayal to video program content.Particularly especially can't accurate description to physical features such as the color of video frequency program, textures.Even voice, this class of captions are not limited by the feature because of subjective factor, also often make artificial treatment become infeasible because its data volume is huge.
For this reason, people are used for video frequency program with graphical analysis, speech analysis and captions analytical technology and handle, and are main tool with the computing machine, robotization obtain the characteristic information relevant with video program content, for content-based search provides support.
In such system, carry out analyzing and processing to the each side feature of one section video frequency program.
Aspect image, carry out the division of scene and camera lens and extract representative key frame video frequency program, and then key frame is carried out Flame Image Process, with its color, texture, shape form,, represent as vector with mathematics.
Further, also to extract high-layer semantic information in the just aforesaid characteristic,, and they also are expressed as the form of text or mathematics as the people's face that occurs in the key frame, the movement tendency of object etc. from the low-level features of key frame of video.
Aspect audio frequency, handle the people's that occurs in the video frequency program voice, background music etc. with computing machine, convert thereof into character string or have the mathematical form of certain implication.
For example,, can use the method for speech recognition, speech conversion be become the form of phonetic figure or speech figure for the people's who occurs in the video frequency program voice.
For the music that occurs in the video frequency program, also can obtain melody, the tonality feature of music by analysis to its waveform character, or the height variation characteristic of tone etc.
Aspect captions, need the Chinese character that occurs in the identification video image, and it is extracted convert character to.
The characteristic that is called as video frequency program through the data that obtain after the above means processing.The quantity of characteristic is very huge often.As, video frequency program about 30 minutes may comprise the key frame picture more than 500, and the feature of each picture often need several tens in addition the vector of dimensions up to a hundred portrayed; The voice of same one section program often need several million space preservation after the characteristic structure that changes into figure one class.
Therefore, when carrying out Content-based Video Retrieval, it is huge often to be faced with data volume, the problem that recall precision is low.Must manage to solve.Perhaps reduce the data volume of characteristic, perhaps take ad hoc base to dwindle range of search to improve retrieval rate.
Simultaneously, also there is the problem that can't accurately mate in Content-based Video Retrieval.The characteristic of depositing in search condition and the Database Systems often is not hundred-percent coupling.For example, even same individual's image occurred in the search condition image and in the database key frame picture, after these key frames were analyzed, the proper vector that obtains also can not be fully was the same with the characteristic of search condition image.But for video frequency searching, these images but are " meeting " search conditions.Therefore, should carry out the fuzzy matching strategy at the retrieval of proper vector.Need suitable retrieval and search strategy to seek the result that can satisfy condition, and obtain the degree of fuzzy matching.
At present, in the video analysis field, art of image analysis, speech analysis field and captions extract the field, and stem-winding achievement in research has all been arranged.The precision of analyzing has reached certain degree.But, still seldom the achievement in research in above-mentioned field is applied in the actual product at present at home.Combine as for achievement in research, be the Content-based Video Retrieval service, especially beyond example above-mentioned field.
The achievement of video analysis field, art of image analysis, speech analysis field and captions being extracted the field combines, and is aided with other focus technologies, is the Content-based Video Retrieval service jointly, also is faced with very big difficulty and challenge.No matter from design, still the exploitation from reality all also has considerable technological difficulties to need to solve.
Summary of the invention
The objective of the invention is to realize a system that can carry out video frequency searching based on video program content information.This system combines the achievement that video analysis field, art of image analysis, speech analysis field and captions extract the field, and is aided with other focus technologies, is the Content-based Video Retrieval service jointly.
A Content-based Video Retrieval system comprises:
A metadata retrieval server is retrieved the metadata of video frequency program according to the requirement of search dispatching server;
A captions retrieval server is used for the XML file of depositing the program captioned test is retrieved;
A video frequency searching cluster is used for the characteristic of key frame of video is retrieved;
A speech retrieval cluster is used for voice messaging to video frequency program, comprises that pinyin string and phonetic figure retrieve;
A user oriented search interface: message format by appointment receives user's searching request and returns Search Results.
Metadata table in the one described metadata retrieval server comprises program ID, programm name, director, performer, language, the place of production, Class1, type 2, file layout, file size, length, screen width, screen height, program address, the program file name is uploaded the time, last set address is uploaded state, whether must examine program level, the examination sign, the program price, attribute field is closed down in the program brief introduction; Metadata retrieval module wherein comprises one and retrieves the storing process of program, a storing process of retrieving program according to combination condition according to program ID; Metadata typing module wherein comprises the storing process that will specify metadata information to insert database table.
Comprise a database table that is used for storing captions XML file in the one described captions retrieval server, a table that is used for the storage server relevant configuration information, a storing process that reads configuration information, a storing process that is used for the XML retrieval, one is used for to the storing process of database typing XML file and the segmented index of an XML.
-described captions XML document data bank table comprises a program id field, XML filename field and XML file field.
-described server configures information table comprises parameter I D field, parameter name field and parameter value field.
-described the storing process that is used for the XML retrieval comprises keyword logical expression generator program piece and search program piece.
-described video frequency searching cluster comprises a video scene retrieval server and a video frequency searching server.
Comprise a storage scenarios key frame indexed data storehouse table in the-described video scene retrieval server, a storing process that is used for the index typing, a routine package that is used to retrieve the scene key frame.
-described scene key frame index data base table comprises the index id field, three index cluster lower bound vector fields, three index cluster upper bound vector fields, index content nested table, total digital section of key frame and index cluster ultimate range field in the index cluster.
-described index content nested table, comprise clauses and subclauses sign id field, program id field under the key frame, key frame number field, critical frame types field, scene start time field, scene concluding time field, camera lens start time field, camera lens concluding time field, key frame time point field, three key frame characteristic information vector fields.
-described video frequency searching server, comprise a database table of depositing key frame of video XML file, deposit key frame indexed data storehouse table for one, the database table of a service device configuration information, a storing process that is used for typing XML file, a routine package that is used to generate index, a routine package that is used for the search index table, a remote linkage that is used to call video scene retrieval server internal program.
The structure of the scene key frame index data base table of introducing in the structure and 9 of-described key frame of video XML document data bank table is identical, and it also comprises an index content nested table.
The structure of the index content nested table of introducing in-described index content the nested table and 10 is identical.
-described the routine package that is used to generate index comprises that one is used for adding a new images eigenwert routine package of a cluster to; A routine package that is used for by a cluster of appointed threshold value expansion; A storing process that is used to create new cluster (directory entry).
-described the routine package that is used for by a cluster of appointed threshold value expansion, it comprises the primary storage process of an expansion, a storing process that is used to calculate expansion back cluster hypermatrix principal diagonal length, one is used to calculate expansion back cluster and whether has the storing process that overlaps with existing other clusters; Wherein, the maximum permissible value of cluster hypermatrix catercorner length is made as 2.0.
-described being used for added a new images eigenwert routine package of a cluster to, and it comprises a primary storage process that is used to add, and one is used to judge whether the eigenwert of an image belongs to the storing process of certain cluster.
-described the routine package that is used for the search index table, it comprises a retrieval primary storage process, a storing process that calculates the minor increment of search condition image and certain cluster, the storing process of the minimum value of the ultimate range of a calculating search condition image and certain cluster, whether effectively one be used to judge certain cluster program segment.
-described retrieval primary storage process, wherein the maximum permissible value of distance is made as 2.0 between search condition image and the cluster.
-described the routine package that is used for retrieving the scene key frame is identical with the structure of 17 routine packages of introducing that are used for the search index table.
-described speech retrieval cluster, comprising a speech buffer storage retrieval server, an optimum retrieval server of voice and a voice phonetic figure retrieval server.
For this cover system, require it not only can the traditional retrieval of back compatible based on essential informations such as literal, also to provide following search function:
1. based on the retrieval of picture.
System user provides a pictures as search condition, may contain user's interest sight, personage or building in the picture.
Searching system will be sought in database with above-mentioned search condition picture and be complementary, and promptly matching degree reaches the key frame picture of certain thresholding, and the video frequency program fragment at these key frame places is returned to the user.
Searching system is in when retrieval, may be according to the global feature of search condition image, as the color of whole picture, texture, shape etc., retrieve.Also may be local feature,, retrieve as personage's (recognition of face) of occurring in the image, buildings, natural scene etc. according to the search condition image.Even can also retrieve according to the motion feature of object in the search condition image.
2. based on the retrieval of a video segment
System user provides a video segment as search condition, and this fragment is shorter and smaller usually, may be the propaganda film or the fragment of that target video program of user's interest.
The video segment that searching system then at first provides the user is analyzed, and extracts its key frame, database is retrieved as search condition with the characteristic of these key frames then.
Different with simple image retrieval, have certain association between each key frame images that from the video segment that the user provides, extracts, because they are from same video.So when result for retrieval is handled, consider this correlativity.Assurance returns to user's result for retrieval, is the video segment with user search condition coupling, but not a plurality of independently, uncorrelated frame.
For 1 and 2, consider the huge of key frame of video characteristic amount, set up index for characteristic.
Different with the index of traditional content of text, the video frequency feature data index will carry out index to the video feature vector of higher-dimension.The basic thought of high dimensional feature vector index is a cluster.Promptly the proper vector of " similar " is divided into a class, the number of times of comparing when retrieving the minimizing after.
3. based on the retrieval of the online voice of user.
System user uses the online one or more search keys of oral account of microphone.In client, computer software will be done simple analysis to user's voice, convert its voice signal to form that searching system needs, give searching system then and carry out actual retrieval.
Searching system will be retrieved the speech retrieval cluster after obtaining above-mentioned search condition.Find the degree of matching to reach the sound bite of certain thresholding, and the video frequency program fragment at these sound bite places is returned to the user.
Consider the degree of accuracy of the huge of voice feature data amount and retrieval, speech searching system is designed to a distributed retrieval cluster.Adopt cache policies to improve the speed of retrieval.
4. based on the retrieval of a sound bite.
System user provides one section voice document, and as the wav form, content wherein is one section voice.Computer software will be done simple analysis to the voice document that the user provides, and convert its voice signal to form that searching system needs, give searching system then and carry out actual retrieval.
Searching system will be retrieved the speech retrieval cluster after obtaining above-mentioned search condition.Find the degree of matching to reach the sound bite of certain thresholding, and the video frequency program fragment at these sound bite places is returned to the user.
Equally, consider the degree of accuracy of the huge of voice feature data amount and retrieval, speech searching system is designed to a distributed retrieval cluster.Adopt cache policies to improve the speed of retrieval.
5. based on the retrieval of caption information
For system user, different on this retrieval mode and traditional retrieval mode are not directly perceived.The user remains the manual search condition of keying in textual form.These conditions are sent to searching system.
The working method of searching system is also similar with traditional searching system.Only, the target of retrieval is a caption database, and the content in the caption database is not from artificial typing, but the result of captions analysis module processing video programs.
The quantity of video frequency program may be magnanimity, and the caption information amount in while every program is also very huge.Therefore, the content of whole caption database magnanimity especially just.To also set up index for caption information for this reason, improve recall precision.
6. integrated retrieval
For example, when the user provided the character search condition, searching system was understood integrated retrieval metadatabase, caption database, even literal is become phonetic, removed to retrieve speech database.
The present invention combines the achievement that video analysis field, art of image analysis, speech analysis field and captions extract the field, and is aided with other focus technologies, is the Content-based Video Retrieval service jointly.
Description of drawings
Accompanying drawing 1 is the structural drawing of general information searching system
Accompanying drawing 2 is Content-based Video Retrieval system module figure
Accompanying drawing 3 is the system construction drawing of Content-based Video Retrieval system
Accompanying drawing 4 is the process flow diagram of captions retrieval module
Accompanying drawing 5 is the process flow diagram of key frame of video retrieval
Embodiment
The structural drawing of this system as shown in Figure 3.System is divided into following several module.
1. metadata retrieval server;
Metadata is the Word message of manually filling in when programming, and is used for portraying the content information such as exercise question, director, performer, the place of production, brief introduction of video frequency program, and frame per second, resolution, program request expense, whether needs characteristics such as DRM checking.
This part is the artificial module that participates in of unique needs in the total system.
After manually filling these data, it is entered in the metadatabase.
Carrying out simple metadata query, or carrying out when needing the relevant information of Search Results after the content-based inquiry, all will send retrieval request, metadatabase is inquired about to the metadata retrieval server.
2. captions retrieval server;
Obtaining the captions characteristic is exactly the captioned test that occurred in the video frequency program.When captions are analyzed, with these texts and scene that the place belongs to occurs and the start and end time of camera lens saves as the XML file of specified format, and be entered in the caption database.
The retrieval module structure of captions retrieval server as shown in Figure 4.
The search condition of being sent by the search dispatching server is a character string, wherein comprises several search conditions, with specifying separator to separate.
At first, extract different search conditions, and they be connected into the logical expression of designated mode according to the requirement of later search program.Then, the video frequency program captions XML file in the caption database is filtered, the program that comprises search condition in the file is picked out according to this expression formula.At last, retrieval by window condition in the file of electing finds this condition the temporal information that the place belongs to scene and camera lens to occur.
3. video frequency searching cluster;
Huge in view of the characteristic amount of key frame of video, in order to guarantee the response time, the video frequency searching module is designed to a retrieval cluster.This cluster comprises video scene and two servers of video frequency searching.
Though video frequency feature data also is to deposit with the form of XML file,, all key frame of video can be carried out index in order to improve recall precision when input database.
Index adopts the high dimension vector index technology based on the R tree, and its basic thought is: defining the distance between two image feature datas, is a cluster with the image division of phase mutual edge distance in specified scope, promptly the image of " similar " is divided into a class.When retrieving, a search index calculates " minor increment " and " minimum value of ultimate range " between search condition and each cluster, eliminates the far cluster of those and search condition image difference according to these two eigenwerts.At last, image in the cluster that calculating is not eliminated and the distance between the search condition image, and ordering is returned.
Like this, just significantly reduce the amount of images of participation comparison and the number of times of calculating, improved the speed of retrieval.
In the two-server of video frequency searching cluster, all use above-mentioned index to represent the key frame images of video frequency program.The cooperation mode of two-server as shown in Figure 5.
(1) video scene retrieval server:
Here deposit all video scene key frame clusters.Because in a video frequency program, the scene quantity of key frames is lacked an order of magnitude than total key frame quantity, and scene key frame itself also has very strong representativeness, so, at first the scene key frame is retrieved, can improve retrieval rate like this.
(2) video frequency searching server:
Here deposit the cluster of all scenes and camera lens key frame.When only retrieving the scene key frame and be not being met necessarily required result for retrieval, retrieve all key frames, with the result of really being mated.
The matching algorithm of key frame of video is a fuzzy matching algorithm.As long as be that the matching degree of key frame in the database and search condition image reaches certain thresholding and can be accepted.
4. speech retrieval cluster;
When the voice of video frequency program are analyzed, will obtain the phonetic figure of voice, what which said figure can obtain to occur in the program by search phonetic.But, the voice messaging of a program about 30 minutes needs the above phonetic figure of 600 width of cloth to be portrayed, and the search speed of phonetic figure itself is not high yet, therefore, in order to guarantee the retrieval rate of searching system, with the speech retrieval partial design is a retrieval cluster, comprises that speech buffer storage retrieval, the retrieval of voice optimization and voice phonetic figure retrieve three retrieval servers.
This three station server has guaranteed that the user can retrieve the voice messaging of those " often accessed ", i.e. information in the speech buffer storage fast.When not having information needed in the speech buffer storage, the content in the retrieval voice optimization server is promptly retrieved from a small amount of more excellent result of voice analysis.Simultaneously, those conditions that the background program of retrieval server will use user search to cross are carried out comprehensive retrieval of off-line to voice phonetic figure, and the result that will obtain is updated in the buffer memory.Like this, just improved user's retrieval rate after this.
About the detailed description of this part, see also patent " distributing speech searching system ".
As previously described.For the optimum pinyin string of voice, voice phonetic figure, video feature vector and caption information all is that form with the XML file passes to database.
1. the core content of the optimum pinyin string XML of voice file comprises: position (start and end times of scene, camera lens, voice) appears in optimum pinyin string content, the posterior probability of optimum pinyin string, optimum pinyin string.Optimum pinyin string is the higher phonetic graph search result of posterior probability who extracts from voice phonetic figure according to certain threshold requirement.
2. the core content of voice phonetic figure XML file comprises: the node of the phonetic figure of one section voice correspondence and arc information, position (start and end times of scene, camera lens, voice) appears in phonetic figure.
3. the core content of video feature vector XML file comprises: the classification of a key frame (scene or camera lens), the color moment characteristic of key frame images, the color histogram characteristic of key frame images, the textural characteristics data of key frame images, the start and end time of scene, camera lens under the key frame, the time point of key frame.
4. the core content of captions XML file comprises: caption content (text formatting), the appearance position of captions (start and end times of scene, camera lens and this section captions).
In above-mentioned retrieval module, used following index.
1.XML segmented index.For captions and voice optimization database, all be directly to retrieve the XML file, therefore to most crucial content in the XML file, promptly voice pinyin string and captioned test are set up segmented index, to improve retrieval rate.
When insertion, renewal, delete database content, carry out synchronously above-mentioned XML segmented index.
At set intervals, carry out Optimizing operation to above-mentioned XML segmented index.This work is designed to the background job of retrieval server, every scheduling in 15 days once.
2. based on the high dimension vector index of R tree.Key frame of video characteristic (representing with the high dimension vector form) has been set up the index of setting based on R.
At present, the achievement in research based on the index technology of R tree is a lot.Native system uses the thought of " first first index ", and the key frame images that is introduced into database is preferentially set up index, and the key frame images of putting in storage is subsequently checked then they can be divided in existing which cluster.If they do not belong to any existing cluster, then go to expand successively existing cluster with them, expand successful condition and be: the cluster (being the cluster hypermatrix) that the ultimate range (being the principal diagonal length of cluster hypermatrix) in the cluster of expansion back between the key frame is no more than after appointed threshold and the expansion does not intersect with other existing clusters.
3. plain text index.Some hot spot field that comprises metadatabase, as director, performer, brief introduction etc., and the keyword of speech buffer storage database.
Claims (20)
1. Content-based Video Retrieval system is characterized in that: comprising:
A metadata retrieval server is retrieved the metadata of video frequency program according to the requirement of search dispatching server;
A captions retrieval server is used for the XML file of depositing the program captioned test is retrieved;
A video frequency searching cluster is used for the characteristic of key frame of video is retrieved;
A speech retrieval cluster is used for voice messaging to video frequency program, comprises that pinyin string and phonetic figure retrieve;
A user oriented search interface: message format by appointment receives user's searching request and returns Search Results.
2. Content-based Video Retrieval as claimed in claim 1 system, it is characterized in that: the metadata table in the metadata retrieval server comprises program ID, programm name, director, performer, language, the place of production, Class1, type 2, file layout, file size, length, screen width, screen height, program address, the program file name is uploaded the time, last set address is uploaded state, whether must examine program level, the examination sign, the program price, attribute field is closed down in the program brief introduction; Metadata retrieval module wherein comprises one and retrieves the storing process of program, a storing process of retrieving program according to combination condition according to program ID; Metadata typing module wherein comprises the storing process that will specify metadata information to insert database table.
3. Content-based Video Retrieval as claimed in claim 1 system, it is characterized in that: comprise a database table that is used for storing captions XML file in the captions retrieval server, a table that is used for the storage server relevant configuration information, a storing process that reads configuration information, a storing process that is used for the XML retrieval, one is used for to the storing process of database typing XML file and the segmented index of an XML.
4. Content-based Video Retrieval as claimed in claim 3 system, it is characterized in that: captions XML document data bank table comprises a program id field, XML filename field and XML file field.
5. Content-based Video Retrieval as claimed in claim 3 system, it is characterized in that: the server configures information table comprises parameter I D field, parameter name field and parameter value field.
6. Content-based Video Retrieval as claimed in claim 3 system is characterized in that: be used for the storing process of XML retrieval, comprise keyword logical expression generator program piece and search program piece.
7. Content-based Video Retrieval as claimed in claim 1 system, it is characterized in that: the video frequency searching cluster comprises a video scene retrieval server and a video frequency searching server.
8. Content-based Video Retrieval as claimed in claim 7 system, it is characterized in that: comprise a storage scenarios key frame indexed data storehouse table in the video scene retrieval server, a storing process that is used for the index typing, a routine package that is used to retrieve the scene key frame.
9. Content-based Video Retrieval as claimed in claim 8 system, it is characterized in that: scene key frame index data base table, comprise the index id field, three index cluster lower bound vector fields, three index cluster upper bound vector fields, total digital section of key frame and index cluster ultimate range field in the index content nested table, index cluster.
10. Content-based Video Retrieval as claimed in claim 9 system, it is characterized in that: the index content nested table comprises clauses and subclauses sign id field, program id field under the key frame, the key frame number field, the critical frame types field, scene start time field, scene concluding time field, camera lens start time field, camera lens concluding time field, key frame time point field, three key frame characteristic information vector fields.
11. Content-based Video Retrieval as claimed in claim 7 system, it is characterized in that: the video frequency searching server, comprise a database table of depositing key frame of video XML file, deposit key frame indexed data storehouse table for one, the database table of a service device configuration information, a storing process that is used for typing XML file, a routine package that is used to generate index, a routine package that is used for the search index table, a remote linkage that is used to call video scene retrieval server internal program.
12. Content-based Video Retrieval as claimed in claim 11 system is characterized in that: the structure of the scene key frame index data base table of introducing in the structure and 9 of key frame of video XML document data bank table is identical, and it also comprises an index content nested table.
13. Content-based Video Retrieval as claimed in claim 12 system is characterized in that: the structure of the index content nested table of introducing in the index content nested table and 10 is identical.
14. Content-based Video Retrieval as claimed in claim 11 system is characterized in that: be used to generate the routine package of index, comprise that one is used for adding a new images eigenwert routine package of a cluster to; A routine package that is used for by a cluster of appointed threshold value expansion; A storing process that is used to create new cluster (directory entry).
15. the Content-based Video Retrieval system that uses as claimed in claim 14, it is characterized in that: be used for routine package by a cluster of appointed threshold value expansion, it comprises the primary storage process of an expansion, a storing process that is used to calculate expansion back cluster hypermatrix principal diagonal length, one is used to calculate expansion back cluster and whether has the storing process that overlaps with existing other clusters; Wherein, the maximum permissible value of cluster hypermatrix catercorner length is made as 2.0.
16. Content-based Video Retrieval as claimed in claim 14 system, it is characterized in that: be used for adding a new images eigenwert routine package of a cluster to, it comprises a primary storage process that is used to add, and one is used to judge whether the eigenwert of an image belongs to the storing process of certain cluster.
17. Content-based Video Retrieval as claimed in claim 11 system, it is characterized in that: the routine package that is used for the search index table, it comprises a retrieval primary storage process, a storing process that calculates the minor increment of search condition image and certain cluster, the storing process of the minimum value of the ultimate range of a calculating search condition image and certain cluster, whether effectively one be used to judge certain cluster program segment.
18. Content-based Video Retrieval as claimed in claim 17 system is characterized in that: retrieval primary storage process, wherein the maximum permissible value of distance is made as 2.0 between search condition image and the cluster.
19. Content-based Video Retrieval as claimed in claim 8 system is characterized in that: the routine package that is used for retrieving the scene key frame is identical with the structure of 17 routine packages of introducing that are used for the search index table.
20. Content-based Video Retrieval as claimed in claim 1 system is characterized in that: the speech retrieval cluster, comprising a speech buffer storage retrieval server, an optimum retrieval server of voice and a voice phonetic figure retrieval server.
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| CN2006101408329A CN101021855B (en) | 2006-10-11 | 2006-10-11 | Video searching system based on content |
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| CN2006101408329A CN101021855B (en) | 2006-10-11 | 2006-10-11 | Video searching system based on content |
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| CN101021855A true CN101021855A (en) | 2007-08-22 |
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