Video Copy Detection Based on Fusion of Spatio-temporal Features
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1 Vdeo Copy Detecton Based on Fuson of Spato-temporal Features BAO We, JI Lxn, GAO Shln, LI Xng, Lu Lxong Natonal Dgtal swtchng System Engneerng & Technologcal R&D Center Zhengzhou, Chna Abstract A vdeo copy detecton method based on fuson of spato-temporal features s proposed n ths paper. Frstly, trajectores are bult and lens boundares are detected by SURF features analyzng, then normalzed hstogram s used to descrbe spato-temporal behavor of trajectores, the bag of vsual words s constructed by trajectores behavor clusterng, word frequency vectors and SURF features wth behavor labels are extracted to express spato-temporal content of lens, fnally, duplcates are detected effcently based on gradematch. The expermental results show the performance of ths method s mproved greatly compared wth other smlar methods. Keywords-Vdeo copy detecton; Speeded-up robust feature; Spato-temporal behavor of trajectores; Bag of vsual words; Grade-match. I. INTRODUCTION Wth the development of multmeda technology and the popularty of broadband servces, many webstes allow users to upload and share vdeo. Users can create vdeo through moble phone and dgtal camera, or drectly download webste vdeo to edt and then upload t to share. There are a large number of copy vdeos on the webstes, whch nfrnge the copyrght of author serously. In order to protect ntellectual property rghts, scholars propose content-based vdeo copy detecton technology, abbrevated as vdeo copy detecton. Vdeo copy detecton has been wdely used n areas such as copyrght protecton, busness ntellgence, ad trackng and content regulaton [1]. The valdty of vdeo copy detecton algorthm s manly dependent on the robustness and dstngushablty of the feature. Most vdeo copy detecton algorthms based on global feature extract low-level feature from the vdeo mage to represent the vdeo, but these algorthms are senstve to varous copy technques, so the detecton result s not satsfactory. Local feature descrbes the structure and texture nformaton of neghborhood of the nterest pont, havng a good robustness generally to brghtness, vewng angle, geometry and affne transformatons. Local feature s wdely used n vdeo copy detecton n recent years, and has a good detecton performance. However, wth the mprovement of vdeo resoluton and the explosve growth of the amount of vdeo, the number of local features extracted from large vdeo database s ncreasngly large. And a mass of nterframe redundances, a huge amount of computaton for smlarty measure and memory overhead become the man problems that lmt ts applcaton. In addton, the majorty of vdeo copy detecton algorthms based on local feature always use the frame-match. These algorthms do not utlze the tme doman nformaton of vdeo. Two rrelevant local features may match, whch results n false detecton. For the above ssues, Law-To [2] proposes a vdeo copy detecton algorthm on the trajectory of feature ponts. Frst, t extracts feature ponts from each frame of vdeo and bulds the trajectory by matchng feature ponts. Second, cluster spato-temporal behavor of trajectores and assgn labels havng semantc nformaton. Fnally, create an ndex for feature ponts, whch not only reduces the probablty of msmatch, but also mproves the effcency of match. By the experment, they proved ts superor detecton performance [3]. Basng on ths, many scholars propose the mproved algorthm. These mproved algorthms can be summed up nto two categores: one s that Sh Chen [4] etc. use U- SURF algorthm to extract feature ponts, match feature ponts to buld trajectores, and descrbe spato-temporal behavor of trajectores by space coordnates and tme coordnates of the feature ponts. Trajectores are dvded nto four categores, ncludng statonary, horzontal movement, vertcal movement and complex movement. Fnally they use local senstve hash (LSH) to create ndex to accelerate the match process. The other s that Guo Junbo [5], Wu Xao [6] use Harrs combned wth KLT algorthm to rapdly extract trajectores. The vdeo s dvded nto sublenses. They quantfy relatve dsplacement of adjacent feature ponts on one trajectory, descrbe the spato-temporal behavor of trajectores by normalzed hstogram or Markov model, and cluster trajectores to buld a bag of vsual words. A word frequency vector represents a sub-lens for fast vdeo copy detecton. These two mproved methods both have some flaws, the former drawback s the huge amount of calculaton of feature extracton and matchng, whch cause tme-consumng serously; the shortcomngs of the latter s the feature contans only tme-doman nformaton but lacks dstngushablty and robustness, and detecton result s not satsfactory. Amng at the drawbacks of the above algorthms, fusng the advantages of the above algorthms, we propose a new vdeo copy detecton method. Frst of all, the SURF features are extracted from each frame, then we buld the trajectores and splt lens by analyzng SURF features, then use statstcal normalzed hstograms to descrbe the spatotemporal behavor of trajectores. Second, cluster trajectores behavor to buld a bag of vsual words, calculate word frequency vector descrbng lens, and fnally use gradematch strategy of word frequency vector and SURF features wth dynamc behavor label for quck match. The expermental result on MUSCLE-VCD-2007 database [7] 0255
2 shows the method not only mantan good detecton effect but also greatly mprove the detecton speed, more sutable for large and complex databases vdeo copy detecton. II. OVERVIEW OF THE PROPOSED ALGORITHM PROCESSES The whole algorthm s dvded nto offlne and onlne part, as s shown n Fg. 1. The processng steps of Offlne part are as follows: We frstly extract the SURF features of each frame of vdeo, buld trajectory and splt lens by analyzng the SURF features; Then, quantfy and encode the relatve dsplacement of adjacent ponts along the trajectory, statstcally generate normalzed hstogram to descrbe spatotemporal behavor of trajectores; Cluster the normalzed hstograms of spatotemporal behavor of trajectores for all vdeo, then we regard cluster center as word to buld a bag of vsual words; The spato-temporal behavor of trajectores along the lens are takng as a word, and the bag of vsual words s used for expressng tme doman nformaton of each lens as a word frequency vector; Word frequency vector and SURF features sets wth the label of dynamc behavor are extracted from each lens of the vdeos n the vdeo lbrary. All these are regarded as the reference vdeo template. Fgure 1. Algorthm flowchart. Onlne process conssts of feature extracton and gradematch two phases. In the feature extracton phase, the bag of vsual words generated n offlne module are used to extracted word frequency vector for each lens, and characterze each SURF feature to form a feature set wth the label of dynamc behavors. In the grade-match phase, matchng s conducted between lens word frequency vectors of the query vdeo and vectors of each reference vdeo to dentfy the most smlar lens; then SURF features wth the label of dynamc behavor are used for exact-match to determne detecton results. III. FEATURE EXTRACTION Feature extracton ncludes buldng trajectory and lens segmentaton, spato-temporal behavor of trajectores descrpton, spato-temporal feature extracton three steps. The purpose of feature extracton s to extract lens word frequency vector and SURF feature set wth label of dynamc behavor to descrbe each lens spato-temporal content. A. buldng trajectory and lens segmentaton Standard SURF algorthm has been selected to extract local features n ths paper. SURF algorthm was put forward by Bay etc. [8] n 2006, wth a hgher detecton speed, characterzng flexbly, more robust. In 2007, Bauer[9], Luo[10], respectvely made an expermental comparson between SURF algorthm and other manstream algorthm. The results show SURF features good robustness to the common vdeo copy transformaton, and the speed s sgnfcantly better than other algorthms. Vdeo s a sequence of frames. Per second vdeo generally contans 25 to 30 frames. Each frame wthn one lens contans smlar content. If we smply rollup SURF features of each frame n a vdeo, there are a lot of redundancy and huge amount of data n the feature set. It s dffcult to measure ther smlarty. After extracted SURF features from each frame of vdeo, ths paper use feature matchng method to buld trajectory and splt lens. Thus the nter-frame redundancy of SURF features wll be elmnated and t wll also remove sngular pont. The smple and stable SURF feature set wll be ganed to represent each lens. Sngular pont s an solated feature ponts that do not match 0256
3 other SURF features nsde the lens. The trajectory buldng and the lens segmentaton processes are as follows: 1) In ths step, SURF features of each vdeo frame are extracted and then perform the followng steps to the end. 2) Sequentally match the current frame SURF feature sets (number of features as m) wth a set of the trajectores and the features do not nclude trajectores of precedng 15 frames : When matchng wth one trajectory n the trajectory sets, the feature s added to the trajectory. The trajectory parameters are updated, and the features are removed from the feature sets of current frame. Record the updated number of trajectores as n 1 ; When a feature matches wth a feature n the precedng adjacent 15 frame sets, a new trajectory s bult. Then record the number of the new trajectores as n 3, the total number of the trajectores s n ; 3) If ( n + n ) n< ϕ&&( n + n ) m< φ, the lens converson occurs, then perform step 4), otherwse go to step 2). ϕ and φ should be set accordng to the expermental result, n ths paper we choose 0.2; 4) Calculate the mean of each trajectory SURF feature as a descrpton of man spatal content of the lens. Record the spatal coordnate of each pont on the trajectory as the descrpton of the temporal domans dynamc behavor. Then splt lens and repeat step 2) and step 3) n the new lens. Through the above processes, the vdeo s dvded nto a group of lenses. Each lens s descrbed wth a SURF feature set and a trajectory set. The SURF feature set s the descrpton of the steady content of the vdeo. The spatal coordnate of each pont along the trajectory s the descrpton of the target pont s dynamc behavor n subsequent frames. Two together can complete show the vdeo content. B. The spato-temporal behavor of trajectores descrpton Usng space coordnates to descrbe the spato-temporal behavor of trajectores exsts two problems: one s that t s senstve to some copy transformatons, such as pcture-npcture, geometry transformaton, local varatons, droppng frames and other transformatons n the tme doman; the other s that the trajectory length s not unform, t s dffcult to measure the smlarty. In ths paper, we quantfy and encode the relatve dsplacement of adjacent ponts along the trajectory and then, statstcally generate a normalzed hstogram to descrbe the spato-temporal behavor of trajectores. The prncple s shown n Fg. 2. The spatotemporal behavor of trajectores are dvded nto two states, statonary and movement. Statonary means relatve dsplacement s less than a certan threshold. Movement means relatve dsplacement s greater than the threshold. To ncrease the dstngushablty, the relatve dsplacement s dvded nto three lengths and eght drectons. Coupled wth statonary, there are a total of 25 states, correspondng wth 25 code. The quantfcaton of the relatve dsplacement should be set accordng to the vdeo resoluton and frame rate. P 0 P 1 P 2 P P 5 6 P P 3 4 P 0 P 1 P 1 P 2 Fgure 2. Spato-temporal behavor of trajectores descrpton dagram. In order to enhance the robustness of spato-temporal behavor feature and the stablty of the trajectory, we remove the trajectory whose length s less than a certan threshold and normalze the codng hstogram. The ultmate spato-temporal behavor of trajectores are represented as 25-dmensonal feature vector: T = { t,, t,, t }. (1) 0 24 Where t = m M ndcates the frequency of code, M s the length of the trajectory, and m s the codng s occurrences along the trajectory. Descrpton of the Normalzed hstogram has good robustness, and can effectvely reduce the mpact of the above transformatons and mprove detecton accuracy. E.g. due to the effect of tme doman transform, f the trajectory whose length s M drops x frames and adds y frames, then the effect of dynamc behavor s x + y. But the effect on the normalzed hstogram s ( x + y+ z)/ M. When z << M, It means the effect to be reduced Mx n general case. C. The vdeo spato-temporal features extracton Copy vdeo and source vdeo may vary consderably on vsual content, but the semantc s the same. To extract rcher semantc nformaton, we frst cluster the spato-temporal behavor of trajectores and buld a bag of vsual words based on clusterng results. Then each lens s regarded as an artcle takng the spato-temporal behavor of trajectores as words. Calculate Frequency vector usng (3) to represent lens. In order to elmnate the msmatch, accordng to the clusterng results, we gve each SURF feature dynamc behavor label, and then store the sorted SURF features wth the dynamc behavor label for quckly matchng. T1 = { t1,, t } 25 T = { t,, t } ( T1; SURF mean 1) ( T2; SURF mean 2) V = {, t, t } 1 K + SURF mean1 SURFmean2 + Label + Label Fgure 3. Schematc dagram for lens word frequency vector and SURF feature ndex buldng. 0257
4 Good clusterng results can buld a more approprate bag of vsual words, and get lens frequency vector and dynamc behavor label easer to dstngush. Compared wth K-Means clusterng algorthm, AP [11] cluster algorthm s more sutable for processng the large-scale database cluster problem, and can get better clusterng results n shorter tme [12]. Ths paper uses AP cluster algorthm mproved by Wang kajun etc. [12] to cluster spato-temporal behavor of trajectores. The cluster algorthm can specfy the number of clusters. We regard cluster center as vsual keyword, each SURF feature n one cluster s marked by t and then buld a bag of vsual words based on codes from vsual keywords. As s shown n Fg. 3, take spato-temporal behavor of each trajectory along the lens as the words, each lens s represented by a 25dmensonal word frequency vector usng the bag of vsual words: V = { t,, t,, t }. (2) 1 d K n N d t = log. (3) n n Where n d s the occurrence of spato-temporal behavor feature of trajectores along the lens d, and n d s the number of the trajectores along the lens d, n represents the number of the lens whch contans spato-temporal behavor feature of trajectores, whle N represents the total number of the lens. IV. GRADE-MATCH In the former secton, the vdeo s dvded nto a set of lenses. The SURF feature set wth dynamc behavor labels and word frequency vector are used to represent each lens. The SURF feature set s a descrpton of the lens spatal nformaton and huge; word frequency vector s an overvew representaton of the lens tme doman nformaton. The lens s one of the contnuous shootng of the camera frame sequence and the vdeo s smallest structural unt. When the query vdeo contans a reference vdeo lens, t s confrmed that the query vdeo s a copy of the reference vdeo. To smplfy the match process and mprove the match effcency, ths paper uses a grade-match strategy shown n Fg. 4. Frst, the lens word frequency vector match: If t can determne that a query vdeo lens s a copy of one reference vdeo, then output the result; If unsure, dentfy the most smlar lens, then perform the SURF feature matchng, two weghted smlarty determne the fnal nspecton results. S S2 S1< α S S S S < β 2 1 Fgure 4. Grade-match flowchart. The query vdeo takes lens as unt, matches sequentally. In ths paper, the cosne dstance measures the smlarty of the word frequency vector along the lens. Thus obtan the N lenses gettng the hghest smlarty score, the score n descendng order are ( D ; S ),( D ; S ),,( D ; S ). Where n N D represents a lens of the reference vdeo lbrary, and S represents the smlarty. If S 2 S 1 s less than a certan threshold, t s consdered that the query vdeo s a copy of the vdeo whch contans lens D. Otherwse, n ths N lens 1 range, use the followng method to measure the smlarty of the SURF feature sets: Assume that the two SURF features sets are respectvely A and B, and the number of ther features s respectvely KeyNum and KeyNum. We use A B the Eucldean dstance as the measure, the number of matchng features s KeyMatch. Accordng to Equaton (4), AB calculate the smlarty of SURF features sets, the results are respectvely ( D ; S ),( D ; S ),,( D ; S ). We calculate the n N fnal smlarty wth the weghted (5), and the results n descendng order are S, S,..., S. If N s less than a 1 2 N certan threshold value, t s consdered that the query vdeo s a copy of the vdeo whch contans the most smlar lens. Otherwse, the query vdeo s not a reference to a copy of the vdeos n the vdeo lbrary. Accordng to the expermental results, the N s 30. KeyMatchAB S = 100%. (4) mn( KeyNum, KeyNum ) A S γ S η S B = +. (5) Assumng that the reference vdeo lbrary contans x lenses, and each lens contans y trajectores n average. Query vdeo contans x lens and y trajectores per lens. Thus the calculaton amount of grade-match strategy s: ( σ σ K) K 64 x x + y y N. (6) 0258
5 If usng the feature pont matchng frstly, and then match trajectory to confrm, the calculaton amount s: x y ( x y σ K + σ N). (7) Where N s the number of the canddate trajectores. K s the number of clusters of the spato-temporal behavor of trajectores. σ s the unt computaton amount calculatng 64 smlarty of 64-dmensonal SURF features. σ 25 s the unt calculaton amount of smlarty of 25-dmensonal spatotemporal behavor of trajectores. σ K s the unt calculaton amount of the smlarty of K -dmensonal word frequency vector. We can fnd the grade-match can effectvely reduce the amount of calculaton. V. EXPERIMENTAL RESULTS AND ANALYSIS A. Expermental set Our experment was conducted on the Intel dual-core 2.9GHz CPU, 2GB RAM computer, Matlab and C language mxed programmng. Reference vdeo set s MUSCLE-VCD database [7].The vdeo set contans 101 reference vdeos, about 100 hours. Nonreference vdeos are downloaded from open-vdeo webste. The query vdeo s made wth TRECVID [1] offcal program. There are total 300 query vdeos, about 12 hours. The query vdeos consst of copy vdeos of the 10 transformaton types. Vdeo format s MPEG-1. B. Evaluaton standard Two evaluaton crtera of TRECVID 2011[1] vdeo copy detecton competton are used to measure the performance of the algorthm: 1) Normalzed detecton cost rate(ndcr): NDCR = P + β R. (8) mss FA mss targ et FA β = C ( C R ). (9) Where P s the undetected rate, R mss FA s the false detecton rate, β s a weghtng coeffcent, C = C = 1 are the cost of false and mssed detectons, FA mss R = / h. The smaller NDCR s, the smaller the targ et cost of vdeo copy detecton algorthm, the better the performance. 2) The average detecton tme T mean : T = T N. (10) mean full quers Where T s the tme of the whole process for all vdeo full from decodng to outputtng a detecton result, N quers represents the number of the query vdeos. The smaller T, the faster the algorthm detecton s. the mean C. Performance Analyss We extracted 31,473,425 trajectores from 58 hours reference vdeos, whch contan 33,804 lenses. In order to mprove the stablty of the trajectores, the short trajectores whose lengths are less than 3 have been removed. The remanng 29,461,003 trajectores are used for AP cluster to buld a bag of vsual words. 50 query vdeos have been selected randomly. Compare the detecton results of dfferent number of clusters, and determne the optmal number of clusters. Fg. 5 shows when K s 82, the detecton results are best. In order to verfy the effectveness of our algorthm, under the above expermental envronment, we made an expermental comparson between algorthm [2], [4], [6] and our algorthm n ths paper. The evaluaton crtera are NDCR and T. The results are shown n Fg. 6 and Table I. mean We can see that the detecton of our algorthm s better than the algorthm proposed n [6] and [2]. The result s near wth the algorthm of [4]. However, the average detecton tme s much shorter than [2] and [4]. As s shown n Fg. 6, our algorthm s optmal to the followng transformatons: affne transformaton (T1), re-encodng (T4) and a slght drop qualty (T6). The reason s we used the standard SURF algorthm to extract the local features whch have good robustness to affne transformaton. Descrbng the spatotemporal behavor of trajectores by normalzed hstogram can effectvely reduce the nfluence of tme-doman varaton. Fg. 6 also shows our algorthm s not as good as the algorthm of [4] for some complex transformatons (T9), (T10). The reason s that word frequency vector cannot completely elmnate the mpact of complex transformatons, whch cause mss detecton. The results can be mproved by ncreasng the number N of canddate lens, but t wll reduce the effcency of detecton. To set the value of N, t should be based on the complexty of copy transformatons and the sze of vdeo lbrares. TABLE I. DETECTION RESULT OF THE ALGORITHMS Algorthm Average NDCR The average Average tme for Average tme for detecton tme (s) feature extracton (s) feature matchng (s) Algorthm of Ref [2] Algorthm of Ref [4] Algorthm of Ref [6] Our algorthm
6 NDCR K Fgure 5. Detecton performance under dfferent number of clusters. NDCR Algorthm of Ref [2] Algorthm of Ref [4] Algorthm of Ref [6] Our algorthm 0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 Transformaton types Fgure 6. Comparson of NDCR Among the 10 knds of the varous algorthms for copes transformaton. As s shown n Table I, n the case of detecton results are smlar, our method s most effcent, especally on the matchng tme. Pont trajectory asynchronous match strategy s adopted by [2]. It only need extract local feature from keyframes(every 29 frames to extract one). The feature extracton speed of [2] s very fast, but t requre to match a large number of feature ponts wth large-scale (tens of mllons) vdeo database s trajectores. Although the ndex structure can speed up the match process, tme-consumng s stll serous. Reference [4] uses the current frame feature sets to match adjacent 15 frames feature sets to buld trajectores. It frstly matches the feature ponts, then confrm the result by trajectory coordnates. Thus feature extracton and matchng both consume a lot of tme. Reference [6] uses Harrs and KLT algorthm to quckly buld the trajectory. It dvded lens nto sub-lens basng on the dfference between current frame and the frst frame of the lens. But the number of sub-lens s large, and t needs ntegrate the matchng result of sub-lens to determne the fnal detecton results, and the matchng s tme-consumng. But [6] mproves the match effcency by ndex, and the detecton speed can satsfy realtme requrement. In ths paper, the SURF feature set and the trajectory set of current frame have been used to match wth adjacent 15 frames feature set that unjon n trajectores to quckly buld the trajectory. Dvde lens basng on the dfference of the current frame and the contents of the entre lens. When matchng, herarchcal polcy has been used to avod massve SURF features matchng, and ths effectvely reduces the match complexty, and mproves the detecton effcency. Whether the query vdeo s reference vdeo copy or not can be determned accordng to the test results of a sngle lens. Ths s why our algorthm s less tme-consumng when matchng. VI. USING THE TEMPLATE In ths paper, we propose the vdeo copy detecton method ntegrated spato-temporal features on prevous work. The method utlzes the fuson of spato-temporal features for analyss, and extracts word frequency vector and SURF feature set wth the label of dynamc behavor to descrbe each lens. It also smplfes the complexty of matchng by classfyng match strategy and mproves the detecton effcency. The expermental results show that the method dramatcally mproves the detecton whle ensurng detecton effect. The next step of the research focuses on how to mprove the speed of buldng trajectores and copy vdeo locatng accuracy. ACKNOWLEDGMENT Ths paper s orgnated from a project numbered as 2011AA010603, whch s supported by natonal 863 foundatons. REFERENCES [1] Gudelnes for the TRECVID 2011CD task Evaluaton[OL]. [2] Law-To J, Busson O, Gouet-Brunet V, et al. Robust Votng Algorthm based on Labels of Behavor for Vdeo Copy Detecton[C]. In ACM Multmeda, [3] Law-To J, L Chen, Alexs Joly, et al. Vdeo Copy Detecton: A Comparatve Study[C]. In Proc of ACM Internatonal Conference on Image and Vdeo Retreval, [4] Sh Chen, Jnqao Wang, Y Ouyang, et al. Mult-Level Trajectory Modelng for Vdeo Copy Detecton[C]. In Proc of IEEE Internatonal Conference on Acoustcs Speech and Sgnal Processng, 2010, [5] Guo Junbo, L Jntao, Zhang Yongdong, et al. Vdeo Copy Detecton Based on Trajectory Behavor Pattern [J]. Journal of computer-aded desgn & computer graphcs, 2010,22, [6] Wu Xao, L Jntao, Tang Sheng, Guo Junbo. Vdeo Copy Detecton Based on Spato-Temporal Trajectory Behavor Feature [J]. Journal of computer research and development, 2010, 47, [7] The orgn of the vdeo database s MUSCLE-VCD-2007[EB/OL]. [8] Bay H, Ess A, Gool L.,etc. SURF: Speeded Up Robust Features[J]. Computer Vson and Image Understandng, 110, [9] Bauer.J, Sünderhauf.N, Protzel.P. Comparng Several Implementatons of Two Recently Publshed Feature Detectors[C]. In Proc. of the Internatonal Conference on Intellgent and Autonomous Systems, Toulouse, France, [10] Luo Juan, Oubong Gwun. A Comparson of SIFT, PCA-SIFT and SURF[J]. Internatonal Journal of Image Processng,3, [11] Frey.J, Dueck.D. Clusterng by Passng Messages between Data Ponts[J]. Scence, 2007, 315, [12] Wang Kajun, Zheng Je. Fast Algorthm of Affnty Propagaton Clusterng under Gven Number of Clusters [J]. applcatons of the computer systems, 2010, 19,
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