CN101404032A - Video retrieval method and system based on contents - Google Patents

Video retrieval method and system based on contents Download PDF

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Publication number
CN101404032A
CN101404032A CNA2008102262681A CN200810226268A CN101404032A CN 101404032 A CN101404032 A CN 101404032A CN A2008102262681 A CNA2008102262681 A CN A2008102262681A CN 200810226268 A CN200810226268 A CN 200810226268A CN 101404032 A CN101404032 A CN 101404032A
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proper vector
hash
video
video frequency
frame
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CN101404032B (en
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尹浩
惠雯
张焕强
黄东
李铮
陈文涛
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Beijing Blue It Technologies Co ltd
Tsinghua University
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Beijing Blue It Technologies Co ltd
Tsinghua University
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Abstract

The invention discloses a video retrieval method based on contents and a system thereof which are used for solving the problem of lower efficiency of the video retrieval based on contents. The method comprises the following steps: a characteristic vector of a video frame to be detected is acquired; a corresponding cartesian coordinate is searched according to the index number of the characteristic vector of a sensitive video frame to be compared in a video fingerprint database and a predefined transformation rule; the characteristic vector of the video frame to be detected is sent to a retrieval module of a corresponding server according to the searched cartesian coordinate; and the retrieval module judges the similarity between the characteristic vector of the video frame to be detected and the characteristic vector of the sensitive video frame to be compared. As a content addressable network is employed to reasonably organize the video fingerprint database, create indexes, and acquire the video example to be detected, the method utilizes a certain retrieval algorithm to search the best matched characteristic vector of the sensitive video frame from the fingerprint database, which improves the video retrieval efficiency.

Description

A kind of Content-based Video Retrieval method and system
Technical field
The invention belongs to the multimedia application field, particularly a kind of Content-based Video Retrieval method and system.
Background technology
Content-based Video Retrieval (CBID) is can be according to the technology of the quick search video object of video content.By feature extraction object video is mapped as point (promptly extracting video finger print) in the high dimension vector space, so just the arest neighbors that the similarity searching problem of object video is converted in the higher dimensional space searches problem.
For mass data, how setting up effective index structure is the key issue that improves retrieval precision and efficient.Popular multi-dimensional indexing technology comprises gridfile, k-d-B tree, quaternary tree, hB tree, R tree and mutation R+ tree and R* tree etc. now, these all are based on the space or based on the division methods of DATA DISTRIBUTION, have good performance under the situation of dimension not too high (below 10 dimensions).
The multi-dimensional indexing technology comprises that also some are suitable for the more indexing means of higher-dimension, as vector approximation method (VA-file), LSH (Locality Sensitive Hashing, local sensitivity Hash table), space filling curve (space-filling curve) etc.
Adopting which kind of multi-dimensional indexing technology in the prior art all is to finish relevant treatment in a station server, and the efficient of Content-based Video Retrieval is lower like this.
Summary of the invention
For the lower problem of efficient that solves Content-based Video Retrieval, the embodiment of the invention provides a kind of Content-based Video Retrieval method, this method is applied to content addressed network, is provided with a plurality of servers on the node in content addressed network cartesian coordinate space, comprising:
Acquisition module obtains the proper vector of frame of video to be detected;
Search the call number of module according to sensitive video frequency frame proper vector to be compared in the video finger print storehouse, and predefined transformation rule, from content addressed network, find the Cartesian coordinates that correspondence is preserved the server of sensitive video frequency frame proper vector to be compared in the Cartesian coordinates of each server;
Sending module sends to the proper vector of frame of video to be detected the retrieval module of corresponding with service device according to the Cartesian coordinates that finds;
Retrieval module is judged the proper vector of frame of video to be detected and the similarity of sensitive video frequency frame proper vector to be compared, and determines the sensitive video frequency proper vector of coupling.
Simultaneously the embodiment of the invention also provides a kind of Content-based Video Retrieval system, comprising:
The video finger print storehouse: be used to preserve the sensitive video frequency frame proper vector with call number, described video finger print storehouse is evenly distributed in a plurality of servers on the content addressed network cartesian coordinate space node;
Acquisition module: the proper vector that is used to obtain frame of video to be detected;
Search module: be used for call number according to video finger print storehouse sensitive video frequency frame to be compared proper vector, and predefined transformation rule, from the Cartesian coordinates of each server, find the Cartesian coordinates that correspondence is preserved the server of sensitive video frequency frame proper vector to be compared;
Sending module: be used for the proper vector of frame of video to be detected being sent to the retrieval module of corresponding with service device according to the Cartesian coordinates that finds;
Retrieval module: be used to judge the proper vector of frame of video to be detected and the similarity of sensitive video frequency frame proper vector to be compared, and determine the sensitive video frequency proper vector of coupling.
The specific embodiments that is provided by the invention described above as can be seen, just because of application content addressing network rationalization is carried out in the video finger print storehouse, set up index, after obtaining video example to be detected, adopt certain searching algorithm from fingerprint base, to search the sensitive video frequency proper vector of mating most, make the efficient of video frequency searching be improved.
Description of drawings
Fig. 1 is the first embodiment method flow diagram provided by the invention;
Fig. 2 is a content addressed network diagram provided by the invention;
Fig. 3 is the second embodiment system construction drawing provided by the invention.
Embodiment
For the lower problem of efficient that solves Content-based Video Retrieval, the embodiment of the invention provides a kind of Content-based Video Retrieval method, to improve recall precision, adapts to the needs of large scale network video frequency searching.When the user imports video example to be measured, adopt certain searching algorithm from fingerprint base, to search the sensitive video frequency frame and the corresponding sensitive video frequency segment of coupling, and pairing similar sensitive video frequency fragment is returned to the user.The video finger print here is meant the frame of video proper vector of extracting from original video data, can represent the content of this video.
Wherein, the vector approximation method can solve the problem of accurate arest neighbors retrieval, and additive method is then only at approximate arest neighbors retrieval.Because video finger print itself is exactly the approximate representation of video content, the arest neighbors of spatial signature vectors does not also mean that arest neighbors on the video content, so even accurately arest neighbors retrieval does not guarantee to obtain Query Result the most accurately yet.And, under many circumstances, select suitable approximate query algorithm can return and the accurate identical result of search algorithm, and have higher efficient.What video frequency searching needed is the balance of a precision and efficient.Particularly in big, higher to the response time requirement occasion of data scale, approximate arest neighbors retrieval will be brought into play more importantly effect, therefore adopt LSH (LocalitySensitive Hashing) algorithm as preferred version.
The LSH algorithm is at first proposed by Indyk and Motwani, utilizes statistical theory, can solve the k-neighbour fast and inquire about problem under the prerequisite that guarantees certain accuracy (with probabilistic manner).Paper " SimilaritySearch in High Dimensions via Hashing " has provided the specific implementation step of this algorithm, its basic thought is, for the point data collection, utilize one group of hash function to set up a plurality of Hash tables with certain constraint condition, make under certain similarity measure condition, the probability that similar point clashes is bigger, and the probability that dissimilar point clashes is less relatively.
First embodiment provided by the invention is a kind of Content-based Video Retrieval method, adopts the LSH algorithm to carry out index in the present embodiment and sets up, and the LSH function definition is: one group of hash function H={h1, ..., hm}, m are positive integer, for data point p, if q is p, distance D (p between the q, q)＜R, then P[hi (q)=hi (p)]＞P1, if D (p, q)＞cR, then P[hi (q)=hi (p)]＜P2.Wherein function P (.) is a probability function, and P1, P2 are given probability, and P1＞P2, i are random number, i ∈ 1 ..., m}.This group hash function is called as so that (P1 P2) is the LSH group of functions of parameter for R, cR.Data point p wherein, different sensitive video frequency frame proper vector in the corresponding present embodiment of q
Figure A20081022626800081
The LSH function is:
Figure A20081022626800082
Wherein vectorial
Figure A20081022626800083
Satisfy normal distribution (Gaussian distribution), w is any real number, and b is any real number between [0, w].Method flow comprises as shown in Figure 1:
Step 101: n sensitive video frequency frame proper vector is mapped among L the Hash table g.
N sensitive video frequency frame proper vector arranged in the video finger print storehouse
Figure A20081022626800084
Adopt the LSH algorithm to carry out index and set up, use L hash function g (.) n sensitive video frequency frame proper vector
Figure A20081022626800085
Be mapped among L the Hash table g, for example: 10 sensitive video frequency frame proper vectors are arranged
Figure A20081022626800086
Figure A20081022626800087
Figure A20081022626800088
Figure A20081022626800089
Figure A200810226268000810
Figure A200810226268000812
Figure A200810226268000813
Figure A200810226268000814
With
Figure A200810226268000815
Wherein
Figure A200810226268000816
Figure A200810226268000817
Figure A200810226268000818
With
Figure A200810226268000820
Be mapped among the Hash table g1 all the other 5
Figure A200810226268000821
Figure A200810226268000822
Figure A200810226268000823
Figure A200810226268000824
With
Figure A200810226268000825
Be mapped among the Hash table g2, adopt different hash function g (.) to be mapped to the sensitive video frequency frame proper vector of Hash table
Figure A200810226268000826
Can be different, as
Figure A200810226268000827
Figure A200810226268000829
With Be mapped among the Hash table g1 all the other 5
Figure A200810226268000832
Figure A200810226268000833
Figure A200810226268000834
With
Figure A200810226268000836
Be mapped among the Hash table g2.
Step 102: the sensitive video frequency frame proper vector among each Hash table g is carried out hash by the LSH function, and the gained result is carried out the secondary hash again, and the sensitive video frequency frame proper vector in each Hash table is mapped in a plurality of hash buckets.
Hash table gj=[h1 wherein (j)..., hk (j)] (j=1,2 ..., L), hi (.) ∈ H (i=2 ... k), wherein H is a LSH family of functions, i.e. one group of hash function H={h1 ..., hm}.As in the g1 Hash table
Figure A200810226268000837
Figure A200810226268000838
Figure A200810226268000839
Figure A200810226268000840
With
Figure A200810226268000841
Carry out the secondary hash, will
Figure A200810226268000842
Figure A200810226268000843
Figure A200810226268000844
Figure A200810226268000845
With
Figure A200810226268000846
Be mapped in 7 hash buckets.Hash table g1=[h1 (1), h2 (1), h3 (1), h4 (1)] expression employing h1 (1), h2 (1), h3 (1), h4 (1)Function will
Figure A200810226268000847
Figure A200810226268000848
Figure A200810226268000849
Figure A200810226268000850
With
Figure A200810226268000851
Be mapped in 7 hash buckets.As pass through h1 (1)Will
Figure A200810226268000852
With
Figure A200810226268000853
Be mapped to first hash bucket, will
Figure A200810226268000854
Figure A200810226268000855
With
Figure A200810226268000856
Be mapped to second hash bucket, pass through h2 (1)Will
Figure A200810226268000857
Figure A200810226268000858
Figure A200810226268000859
Figure A200810226268000860
With
Figure A200810226268000861
Be mapped to the 3rd hash bucket, pass through h3 (1)Will
Figure A20081022626800091
With Be mapped to the 4th hash bucket, will
Figure A20081022626800093
Figure A20081022626800094
With
Figure A20081022626800095
Be mapped to the 5th hash bucket, pass through h4 (1)Will With
Figure A20081022626800097
Be mapped to the 6th hash bucket, will
Figure A20081022626800098
Figure A20081022626800099
With
Figure A200810226268000910
Be mapped to the 7th hash bucket.
Step 103:, determine the Cartesian coordinates of L Hash table numbering correspondence in content addressed network according to the numbering and the predefined transformation rule of L Hash table.
The number table of Hash table g1 is shown binary sequence x, as 0100001010.If
Figure A200810226268000911
Wherein d is the dimension such as the d=2 of Virtual Space, and m is the figure place 10 of binary sequence x.X is divided into groups to a high position from low level, and per 8 is one group, and being divided into is 2 groups (last group can be discontented with 8), and first group is 00001010, the second group is 10, the dimension of 2 in the corresponding Virtual Space.Calculate every group decimal value xi (i=1 ..., d), first group is 10, the second groups is 1, xi%2 dBe the i dimension coordinate of pairing node, promptly the Cartesian coordinates of 0100001010 correspondence is (2,1).
Adopt the call number of the numbering of Hash table g in the present embodiment as sensitive video frequency frame proper vector.
Step 104: according to the corresponding relation of numbering and the Cartesian coordinates of Hash table g, L Hash table (including the sensitive video frequency frame proper vector that is mapped to wherein) be distributed in N the server in the content addressed network preserve, each server all has corresponding Cartesian coordinates, wherein N≤L in content addressed network.
The Cartesian coordinates that No. 8 servers (Hash table is numbered 0100001010) are corresponding is (2,1), preserves Hash table g1 in this server.Wherein have Cartesian coordinates each server content addressed network diagram as shown in Figure 2.
Step 105: the acquisition module of arbitrary server obtains the proper vector of frame of video to be detected in the content addressed network
Figure A200810226268000912
One section video to be detected is arranged from network, and No. 4 servers therefrom obtain the proper vector of the frame of video to be detected of this video in the content addressed network
Figure A200810226268000913
Step 106: obtain
Figure A200810226268000914
Server search module, determine the Cartesian coordinates of this Hash table place server successively according to the numbering of sensitive video frequency frame proper vector to be compared place Hash table.
As sensitive video frequency frame proper vector to be compared in Hash table g1, No. 4 servers search the Cartesian coordinates that module at first will be determined No. 8 servers in Hash table g1 place, determine that according to the numbering of Hash table g1 the Cartesian coordinates of No. 8 server correspondences is (2,1).
Step 107: obtain
Figure A20081022626800101
Server sending module will
Figure A20081022626800102
Send to the retrieval module of the server of storage sensitive video frequency frame proper vector to be compared place Hash table.
The Cartesian coordinates of No. 4 server correspondences is (1,1), is that (2,1) are adjacent because Cartesian coordinates is (1,1) and Cartesian coordinates, and the sending module of No. 4 servers directly will
Figure A20081022626800103
Send to the retrieval module of No. 8 servers.
If acquisition module obtains the proper vector of frame of video to be detected
Figure A20081022626800104
Server be not No. 4 servers but No. 8 servers, then the sending module of No. 8 servers directly sends
Figure A20081022626800105
Retrieval module to book server.
Step 108: the retrieval module of server of storing sensitive video frequency frame proper vector to be compared place Hash table is right
Figure A20081022626800106
Carry out the secondary hash, it is mapped in the hash bucket of Hash table.
Retrieval module is right
Figure A20081022626800107
Carrying out the secondary hash is mapped to it in first hash bucket of Hash table g1.
Step 109: take out with
Figure A20081022626800108
Fall into the sensitive video frequency frame proper vector of same hash bucket, and calculate wherein each sensitive video frequency frame proper vector and
Figure A20081022626800109
Euclidean distance, judge similarity each other, and determine the sensitive video frequency proper vector of coupling.
In first hash bucket of taking-up Hash table g1
Figure A200810226268001010
With
Figure A200810226268001011
And calculate respectively with Euclidean distance, judge similarity each other, and determine
Figure A200810226268001013
For with (similar) sensitive video frequency proper vector of coupling.Up to obtaining abundant similar sensitive video frequency frame proper vector, or relatively finish with whole sensitive video frequency frame proper vectors.
Wherein in the step 102 in order to guarantee the performance of LSH algorithm, need to consider two important parameters here---the number k of LSH function h (.) in the number L of Hash table g and each Hash table.The value of L and k can directly have influence on the performance of this algorithm.Consider following performance index: index Time Created: O (nLkt), wherein t is for calculating required time of each h (.), space: O (the nL)+required space of preservation data point, query time: O (L (kt+dnP 2 k)), should guarantee that L and k have following relation:
L = P 1 - k = ( 1 P 1 ) k
Wherein P1 is a given probability in the LSH function as previously mentioned.
Also can adopt in the present embodiment as the multi-dimensional indexing technology as: gridfile, k-d-B tree, quaternary tree, hB tree, R tree and mutation R+ tree and R* tree etc., these all are based on the space or based on the division methods of DATA DISTRIBUTION, generate the index of each sensitive video frequency frame proper vector in the video finger print storehouse by said method, sensitive video frequency frame proper vector with different index number (as 1-10000), be distributed in 10 servers in the content addressed network and preserve, each server all has corresponding Cartesian coordinates in content addressed network.When Content-based Video Retrieval, after wherein the acquisition module of 3# server obtains the proper vector of frame of video to be detected, the 3# server search the call number 1000 of module according to sensitive video frequency frame proper vector to be compared, and predefined transformation rule, from the Cartesian coordinates of 10 servers, find correspondence and preserve the Cartesian coordinates of server of sensitive video frequency frame proper vector to be compared for (0,0); The sending module of 3# server sends to the proper vector of frame of video to be detected the retrieval module of 2# server (Cartesian coordinates is (0,0)) according to the Cartesian coordinates (0,0) that finds;
The retrieval module of 2# server is judged the proper vector of frame of video to be detected and the similarity of sensitive video frequency frame proper vector to be compared.
Second embodiment provided by the invention is an a kind of Content-based Video Retrieval system, and its structure comprises as shown in Figure 3:
Video finger print storehouse 201: be used to preserve the sensitive video frequency frame proper vector with call number, described video finger print storehouse is evenly distributed in a plurality of servers on the content addressed network cartesian coordinate space node;
Acquisition module 202: the proper vector that is used to obtain frame of video to be detected;
Search module 203: be used for call number according to video finger print storehouse sensitive video frequency frame to be compared proper vector, and predefined transformation rule, from the Cartesian coordinates of each server, find the Cartesian coordinates that correspondence is preserved the server of sensitive video frequency frame proper vector to be compared;
Sending module 204: be used for the proper vector of frame of video to be detected being sent to the retrieval module of corresponding with service device according to the Cartesian coordinates that finds;
Retrieval module 205: be used to judge the proper vector of frame of video to be detected and the similarity of sensitive video frequency frame proper vector to be compared, and determine the sensitive video frequency proper vector of coupling.
Further, the video finger print storehouse 201 of each server comprises Hash table 2011: be used to preserve sensitive video frequency frame proper vector, the numbering of described Hash table is as the call number of sensitive video frequency frame proper vector;
Described system also comprises:
Secondary Hash module 206: be used for using the LSH algorithm that each the sensitive video frequency frame proper vector that is kept at each server Hash table is carried out hash, again the gained result carried out the secondary hash, obtain a plurality of hash buckets;
Hash table 2011 comprises a plurality of hash buckets 20111: each the sensitive video frequency frame proper vector that is used for Hash table is preserved and is carried out the sensitive video frequency frame proper vector after the hash twice after carrying out twice hash;
Retrieval submodule 2051: be used for the proper vector of frame of video to be detected is carried out hash twice, obtain the hash bucket of the correspondence that the proper vector of frame of video to be detected is mapped to;
Judge the similarity of sensitive video frequency frame proper vector to be compared in the proper vector of frame of video to be detected and the corresponding hash bucket.
Further, the secondary Hash module 206: also be used for L hash function g (.) with each sensitive video frequency frame proper vector
Figure A20081022626800121
After being mapped to L the Hash table gj that is kept on each server, by k LSH function hi (.) to each the sensitive video frequency frame proper vector among the Hash table gj Carry out hash, i.e. gj=[h1 (j) ..., hk (j)] (j=1,2 ..., L), hi (.) ∈ H (i=1,2 ... k), H is a LSH family of functions.
Further, retrieval module 205: also be used for the proper vector by calculating frame of video to be detected and the Euclidean distance of sensitive video frequency frame proper vector to be compared and judge similarity each other.
Obviously, those skilled in the art can carry out various changes and modification to the present invention and not break away from the spirit and scope of the present invention.Like this, if of the present invention these are revised and modification belongs within the scope of claim of the present invention and equivalent technologies thereof, then the present invention also is intended to comprise these changes and modification interior.

Claims (10)

1, a kind of Content-based Video Retrieval method is characterized in that, is provided with a plurality of servers on the node in content addressed network cartesian coordinate space, and this method comprises:
Acquisition module obtains the proper vector of frame of video to be detected;
Search the call number of module according to sensitive video frequency frame proper vector to be compared in the video finger print storehouse, and predefined transformation rule, from content addressed network, find the Cartesian coordinates that correspondence is preserved the server of sensitive video frequency frame proper vector to be compared in the Cartesian coordinates of each server;
Sending module sends to the proper vector of frame of video to be detected the retrieval module of corresponding with service device according to the Cartesian coordinates that finds;
Retrieval module is judged the proper vector of frame of video to be detected and the similarity of sensitive video frequency frame proper vector to be compared, and determines the sensitive video frequency proper vector of coupling.
2, the method for claim 1 is characterized in that, also comprises before finding corresponding Cartesian coordinates step:
Use local sensitivity Hash table LSH algorithm that each the sensitive video frequency frame proper vector that is kept in each server Hash table is carried out hash, obtain a plurality of hash buckets, each sensitive video frequency frame proper vector is mapped in each hash bucket, simultaneously with the numbering of each Hash table call number as sensitive video frequency frame proper vector;
Also comprise after sending the retrieval module step of proper vector to the corresponding with service device of frame of video to be detected:
The proper vector of frame of video to be detected is carried out hash twice, obtain the hash bucket of the correspondence that the proper vector of frame of video to be detected is mapped to;
The step of judging characteristic vector similarity is specially:
Judge the similarity of sensitive video frequency frame proper vector to be compared in the proper vector of frame of video to be detected and the corresponding hash bucket.
3, method as claimed in claim 2 is characterized in that, uses the LSH algorithm to carry out hash and be specially being kept at each sensitive video frequency frame proper vector in each server Hash table:
With L hash function g (.) with each sensitive video frequency frame proper vector
Figure A2008102262680002C1
Be mapped among L the Hash table gj that is kept on each server;
By k LSH function hi (.) to each the sensitive video frequency frame proper vector among the Hash table gj
Figure A2008102262680003C1
Carry out hash, i.e. gj=[h1 (j) ..., hk (j)] (j=1,2 ..., L), hi (.) ∈ H (i=1,2 ... k), H is a LSH family of functions.
4, method as claimed in claim 3, it is characterized in that, use the LSH algorithm that each the sensitive video frequency frame proper vector that is kept in each server Hash table is carried out hash, the number k of LSH function hi (.) has following relation in the number L of Hash table and each Hash table:
Figure A2008102262680003C2
Or
Figure A2008102262680003C3
At the LSH of LSH algorithm group of functions H={h1 ..., among the hm}, 2 sensitive video frequency frame proper vectors wherein
Figure A2008102262680003C4
Figure A2008102262680003C5
Between distance D ( v &RightArrow; , v &RightArrow; 2 ) < R , Then P [ hi ( v &RightArrow; 1 ) = hi ( v &RightArrow; 2 ) ] > P 1 , Wherein R is predefined distance, P 1Be predefined probable value.
5, method as claimed in claim 3 is characterized in that, the LSH function is:
Figure A2008102262680003C8
Wherein vectorial
Figure A2008102262680003C9
Satisfy normal distribution (Gaussian distribution), w is any real number, and b is any real number between [0, w].
6, method as claimed in claim 2 is characterized in that, the step of comparative feature vector similarity is specially:
The Euclidean distance of sensitive video frequency frame proper vector to be compared is judged similarity each other in proper vector by calculating frame of video to be detected and the corresponding hash bucket.
7, a kind of Content-based Video Retrieval system is characterized in that, comprising:
The video finger print storehouse: be used to preserve the sensitive video frequency frame proper vector with call number, described video finger print storehouse is evenly distributed in a plurality of servers on the content addressed network cartesian coordinate space node;
Acquisition module: the proper vector that is used to obtain frame of video to be detected;
Search module: be used for call number according to video finger print storehouse sensitive video frequency frame to be compared proper vector, and predefined transformation rule, from the Cartesian coordinates of each server, find the Cartesian coordinates that correspondence is preserved the server of sensitive video frequency frame proper vector to be compared;
Sending module: be used for the proper vector of frame of video to be detected being sent to the retrieval module of corresponding with service device according to the Cartesian coordinates that finds;
Retrieval module: be used to judge the proper vector of frame of video to be detected and the similarity of sensitive video frequency frame proper vector to be compared, and determine the sensitive video frequency proper vector of coupling.
8, system as claimed in claim 7 is characterized in that,
The video finger print storehouse of each server comprises Hash table: be used to preserve sensitive video frequency frame proper vector, the numbering of described Hash table is as the call number of sensitive video frequency frame proper vector;
Described system also comprises:
Secondary Hash module: be used for using the LSH algorithm that each the sensitive video frequency frame proper vector that is kept at each server Hash table is carried out hash, again the gained result carried out the secondary hash, obtain a plurality of hash buckets;
Hash table comprises a plurality of hash buckets: each the sensitive video frequency frame proper vector that is used for Hash table is preserved and is carried out the sensitive video frequency frame proper vector after the hash twice after carrying out twice hash;
Described retrieval module also comprises:
Retrieval submodule: be used for the proper vector of frame of video to be detected is carried out hash twice, obtain the hash bucket of the correspondence that the proper vector of frame of video to be detected is mapped to;
Judge the similarity of sensitive video frequency frame proper vector to be compared in the proper vector of frame of video to be detected and the corresponding hash bucket.
9, system as claimed in claim 8 is characterized in that,
Secondary Hash module: also be used for L hash function g (.) with each sensitive video frequency frame proper vector
Figure A2008102262680004C1
After being mapped to L the Hash table gj that is kept on each server, by k LSH function hi (.) to each the sensitive video frequency frame proper vector among the Hash table gj
Figure A2008102262680004C2
Carry out hash, i.e. gj=[h1 (j) ..., hk (j)] (j=1,2 ..., L), hi (.) ∈ H (i=1,2 ... k), H is a LSH family of functions.
10, system as claimed in claim 7 is characterized in that,
Retrieval module: also be used for the proper vector by calculating frame of video to be detected and the Euclidean distance of sensitive video frequency frame proper vector to be compared and judge similarity each other.
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