SPATIO-TEMPORAL SIGNATURES FOR VIDEO COPY DETECTION
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1 SPATIO-TEMPORAL SIGNATURES FOR VIDEO COPY DETECTION Isabelle Simand, 2 Denis Pellerin, 3 Stephane Bres and 3 Jean-Michel Jolion Isabelle.Simand@liris.cnrs.fr 3 LIRIS, bat. J. Verne, INSA, 6962 Villeurbanne Cedex, FRANCE 2 LIS, 46 av. F. Viallet, 3803 Grenoble Cedex, FRANCE ABSTRACT The number of copied videos is growing rapidly on television broadcast networks as well as on the world wide web. The existing copy detection methods resort either to image techniques or to video ones. We propose a spatio-temporal for the automatic detection of video extracts, based on the evolution of gray level centroids along time. We have obtained good results for a base of more than 50 Gb of data and for numerous tests of robustness. Our algorithm is robust to changes in contrast and brightness, zooms, modification of frame rate, a logo superimposition on the image, etc.. INTRODUCTION The number of copies of a given video grows rapidly on television broadcast networks as well as on the world wide web. That causes problems of law and efficiency, among others for search engines. Video copy detection is a recent research domain also named video monitoring or video fingerprinting. It is generally based on the creation of a compact representation () of each video in a database. Copy detection is performed by comparison between the of a given video and those stored in a database. Thus, this approach is different from basic image or video indexing as the search is based on a strong matching criteria instead of a similarity one. The research in video copy detection or video fingerprinting is recent. The approaches described in the literature can be grouped according to the type of extracted features: spatial features in images or spatio-temporal features in video. Cheung et al. [, 2] have proposed a by selecting a few frames similar to seed images. The introduced by Joly et al. [3] consists in extracting local features around interest points. These two previous techniques are not robust to changes in the order of the images in the video. Indyk et al. [4] use the shot change duration as for a video, but video sequences, especially short one, contain few shot changes. Hampapur et al. [5] propose two sequence-matching methods based on motion direction histograms or average gray level over image blocks, and compare them on three videos. Oostveen et al. [6] consider the sign of the differences of mean luminance over blocks. The is compact and facilitates the research step, but is sensitive to global geometric operations. Finally, Hoad and Zobel [7] use the magnitude of two motion vectors calculated for the darkest (respectively lightest) 5% of the pixels. They evaluate the robustness with several degraded versions of the clips as queries and notice that it is sensitive to changes in luminance and resolution. These previous spatio-temporal methods have been tested on a few hours of video. In this paper we propose a spatio-temporal for the automatic detection of video extracts, based on the evolution of gray level centroids along time. This is compact enough to keep storage and computing time low. Moreover, it is discriminant, and robust to several transformations. Particular applications are the supervision of television broadcast and video control storage to avoid duplication, or the verification of the terms of a contract. In section 2, we present the method for the extraction and the video search. Section 3 includes the results obtained for a base of more than 50 Gb of data and tests of robustness, and section 4 summaries the work. 2.. Signature extraction 2. METHOD We have chosen to characterize each video by calculating the gray level centroid of each frame of the video. The coordinates (c x, c y ) of the centroid (Eq. ) are calculated as the weighted sum of the pixel positions, the weights being the gray values of the pixels: c x = size (L i x i) size L i c y = size (L i y i) size L i () where size is the number of pixels considered in the image, and L i the gray level or the luminance of the pixel. Then, these coordinates are normalized to a single frame size. This computation is not costly for each frame. The resulting point is obviously not significant for one frame, but becomes very characteristic for several successive frames,
2 Figure : Example of motion of a centroid on a few frames. due to its movement during a few seconds. As a consequence, it leads to an efficient spatio-temporal (Fig. ). This corresponds to a fingerprinting approach rather than a watermarking one, because no information is added. This first method has the advantage to be applicable even for videos which are already on the net. On the other hand, this is a low level one and does not allow semantic comparison, which is beyond the scope of this project. This approach is based on the gray levels, obtained from the RGB color components. Indeed, combining these three components in one centroid is faster and simpler than using them separately. As a result, the is identical for a color video and its gray value version. As a video can be related to quite a complex scene, only one centroid is not sufficient to characterize a video extract. We thus propose to divide the frames in several subimages. Based on our experiments, dividing a frame into four quarters is the best compromise between the complexity of the and its discriminative power. For each quarter, a centroid is calculated (Fig. 2). This permits us to keep minimal spatial information on the gray level repartition. Thus, without increasing the number of computations (which depends on the frame size and not on the number of centroids calculated), we passed from to 4 centroids and increased the precision. A test with 6 centroids has shown the limits of this approch: for /6 of the image s size, the motion of a centroid is not much of importance. The computation of the centroids location is biased toward brighter areas in order to amplify the centroids movements. This is done by a look-up table, so without any additional cost (Eq. 2). Then, the motion is less uniform and easier to be discriminated. f(l) = L (2) where L is the original gray level between 0 and 255. The of an image is composed of the eight values of the four centroids coordinates (x, y, x 2, y 2, x 3, y 3, x 4, y 4 ). The of a video consists of the s of its images. We have sub-sampled the image horizontally and vertically to pixel out of 8 to speed up the computation. This selection is founded on the redundancy due to the gray level correlation of neighbor pixels. Effectively, we can notice (Fig. 3) that the variations of a given coordinate are very similar before and after the sub-sampling of pixel out of 64, which has no harmful effect on the characterization of the image. Moreover, we can see that a subsampling of pixel out of 256 leads to noised variations. The is then less characteristic of the video than the previous versions. This sub-sampling strategy is very significant in terms of computation time for the creation of the s associated with a video base. This is even more important during the search phase, which begins with the online generation of the of the video extract to be compared. Variations of x Number of frame Figure 2: Example of a frame extracted from the movie Avengers, on which the four centroids are superimposed (one per quarter) Figure 3: Example of variations of the coordinate x during 0 s. Top: without sub-sampling, center: subsampling of pixel out of 64, bottom: sub-sampling of pixel out of 256
3 The uniqueness of the cannot of course be proved but we shall assume this uniqueness as a consequence of the complexity of the : 8n pixel locations, where n is the number of frames Searching a video using a video extract It is possible to perform fast comparisons in order to check a video toward a database starting from a short query based on the proposed. Among all the possible measures (the L and L 2 distances, the covariance, the correlation,...), the L -distance is the most robust as well as the least expensive in terms of computational requirements. Our process involves the computation of the distances between the centroids positions of the, (x, y) j, and those of the videos of the database, (x, y ) j. For frame i, the distance is (Eq. 3): D i = 4 ( x j x j + y j y j ) (3) j= The distances D i are computed between the images of the query and the images of each video of the base, from the beginning to the end. The queries are composed of 200 frames, which corresponds to 7 or 8 seconds of video. It is comparable to the length of the jingles used in audio experiments [8, 9]. In particular, it is shorter than most of commercials. Among these 200 frames, only one out of two is used for the comparison (Fig. 4). Indeed, 00 D i distances are required to comfort the uniqueness, and sufficient to ensure a good recognition, with a minimum computation cost. Obviously, taking 00 frames in a block of 200 images is more discriminant than choosing them consecutively, since there is generally more motion over 200 frames. To search the video, we use a sliding window which is moved by steps of four frames to reduce the scan time, assuming that during four consecutive frames the motion of the centroids is negligible. More formally, we have: At time t : At time t+ : D = 0 for i = nb nb + 200, i = i + 2 do D = D + D i D = 0 for i = nb + 4 nb , i = i + 2 do D = D + D i The decision about a query depends on an empirically determined adaptive threshold on the global distance values, which can take three values (high=60, medium=45, low=30). The actual value is fixed according to the temporal variance of the centroids coordinates. We call variance of a video extract the sum of the eight coordinate variances over the 00 frames which compose the. A video extract has experimentally been defined as static, moderately dynamic and dynamic when its variance is, respectively, bellow 400, between 400 and 800, and above 800 (Fig. 5 and 6). Recognition threshold Video Variance of the query Request shift (4 frames) t t time Figure 4: Representation of the comparisons between the video and the query, along time. Figure 5: Variation of the recognition threshold depending on the variance of the video extract over 00 frames. The smaller the variance (static scenes), the lower is the recognition threshold, since dynamic scene centroids have characteristic motion, which is less true for static ones. Hence, moderately dynamic and static scenes need a tighter threshold. We can remark that the L distance, (contrary to the correlation method) takes into account the relative position of the centroids in the image, and not only their motion. That is why the method is working even for static scenes.
4 Distance D Number of frame Figure 6: Distance between the video analyzed and the extract used as a request. This video query, a part of advertisement, is correctly located two times in the video, where the peaks are passing over the detection threshold. 3. RESULTS We have built a database of 53 Gb of data in order to obtain significant results. This corresponds to 0 hours of videos (i.e. more than 380 videos). There are English and French TV news, cartoons, advertisements, soap operas, many documentaries from the TREC video 2002 competition [0] and from the AIM corpus developed within the French inter-laboratory research group ISIS. These gray level or color videos are in Mpeg or DivX format. The samples are short video clips or long movies. The experimental protocol consists of a search, in the database, of a video using an extract of video. In order to evaluate our results, we have established a ground truth of our database. It allows us to estimate the effectiveness of the method in terms of recall and precision (R and P). Recall measures the relevant results returned, while precision measures the proportion of results returned that actually are relevant (Eq. 4, 5). In contrast to classical Content Based Image Retrieval, we are not looking for the n best similar videos, i.e. there is no result set window. As the search process ends up with a binary decision for any video, a recall of means that the actual number of copies is extracted and a precision of means that no more wrong videos are detected in the database. For these robustness tests, we have created different versions of 0 videos by changing, each time, one of their characteristics. It is important to indicate that all the searches done during those tests have been realized using the whole database, even if, for each request, we are interested only in a particular aspect of the videos. 3.. Random search in the database We are interested in the search of videos which do exist in the database, therefore we have randomly selected 50 video extracts of the database and used them as requests. The results are shown in Table. Recall in 00% of the cases Precision in 92% of the cases 0.67 in 2% of the cases 0.5 in 4% of the cases 0.07 in 2% of the cases Table : Results obtained for 50 video extracts randomly chosen as request among the 386 videos of the database. Assume that these 50 requests were not in the database. According to these precision ratios, we can conclude that in only 8% of the cases, the system returns a non empty answer Robustness tests within the database Robustness to modification of frame size and zooms Consequently to the normalization of the frame size, the method is not sensitive to global size modification. The zoomed videos on which we have done our experiments have been realized by cutting and resizing the frame to its initial size (Fig 7). The offers a recall of in 00% of the cases for a wide range of variations, and a slight decrease for extreme zooms: indeed, we obtain a recall of 0.9 for a 20% zoom in and 0.8 for a 23% zoom out. The precision is for 9 cases out of 0, and 0.47 for the last one. R = # relevant results returned # relevant results in the entire database (4) P = # relevant results returned # total results returned We give the results of a series of random requests in the database, and several illustrations of the robustness of the in different situations. (5) (a) (b) Figure 7: Example of zoom changes (a) original video, (b) video zoomed in by 20%.
5 Robustness to variations in contrast and brightness As far as contrast is concerned, our experiments have been realized for two types of modifications: an increase of 25% of the contrast, and then a decrease of 25% (Fig. 8, b and c). For these kinds of contrast variations, we obtained a recall of in all the experiments, and a precision of in 90% of the cases, which shows the reliability of the. Regarding brightness, the results were also very satisfying (Fig. 9), for decreases as well for increases of 5%, 0% or 20% (Fig. 8, d and e). The results in terms of recall and precision are recorded in Table 2. Recall Precision in 70% of the cases in 90% of the cases 0.83 in 20% of the cases 0.5 in 0% of the cases 0.82 in 0% of the cases (b) (c) (d) (e) (a) Figure 8: Example of contrast and brightness changes (a) original video, contrast with respect to the original (b) 25%, (c) +25%, brightness with respect to the original (d) 20%, (e) +20%. Nb of videos 0 Table 2: Results obtained after a change in brightness Robustness to change in compression format Our database is exclusively made of videos in Mpeg and DivX formats. Fifteen among these 386 videos are in both formats. They allow us to test the format effect as shown in Table 3. Video requests Compres. Video format num. -2 Mpeg DivX Nb of videos Recall Precision Table 3: Results obtained when the request and the video found are in a different compression format. The recall and precision ratios are maximal in most cases, and reach the value 0.5 in the worst case, which is satisfying. The video number 5 gives different results depending on the format of the request (Mpeg or DivX) Robustness after a noise addition The presence of noise in images is a current phenomenon as soon as a video is manipulated. We have therefore cho % of brightness Figure 9: Number of videos recognized, depending on brightness in the video. In each of this 0 cases, the request is the original sen to add gaussian white noise of variance v to the image, in order to test the robustness of the towards this aspect. Results for this kind of noisy query are shown in Table 4. Observing this table and Figure 0, we notice that the performances of the s are declining when v reaches 0%. Figure illustrates an image disturbed by this kind of noise Robustness to change in frame rate Concerning similar frame rates, such as 24 fps and 25 fps, the method is very robust. For more important variations we have proceeded as follows. The database contains videos at both 25 and 30 fps. In order to test the robustness towards a possible change in frame rate, we have transformed three videos from 25 to 30 fps and three others from 30 to 25 fps, be-
6 Variance Recall Precision (9/0) (8/0) v=2% 0.77 (/0) 0.67 (/0) 0.4 (/0) (6/0) (6/0) v=6% 0.69 (/0) 0.22 (/0) 0.5 (/0) 0.4 (/0) 0 (2/0) 0 (2/0) (3/0) v=0% (5/0) 0.3 (/0) 0 (5/0) 0.09 (/0) 0 (5/0) Table 4: Results obtained for videos distorted by a gaussian white noise of variance v. They are decreasing as the noise is growing. Nb of videos v parameter Original video at 25 fps Extraction of the Original at 25 fps Modification of the frame rate Re-sampling of the Modified video at 30 fps Recognition of the modified video in the base Modified at 30 fps Figure 2: Transformations performed to improve the detection of videos whose frame rates have been modified. transformations. We repeat this process with the s of videos which had a frame rate of 30 fps, and which have been transformed to 25 fps. In Figure 3, the curves represent the same in each of these cases. We can observe that the coordinate variations are nearly the same for the 30 fps video and after re-sampling the original 25 fps video. After this re-sampling, the recognition ratio is better than before. Indeed, we have recorded, in 00% of the cases (6/6), a recall ratio of, and the same precision ratio in 5 cases out of 6. Variations of x Figure 0: Number of original videos recognized using a noisy query image, depending on the value of the variance parameter applied Number of frame (a) (b) Figure : Example of noise addition (a) original video without noise, (b) noised video: variance v=0%. fore extracting their s. The results obtained by comparing the s at 25 fps and 30 fps were not satisfying. We then decided to proceed to their re-sampling. As a result, the s of the original videos at 25 fps have been re-sampled to create modified s at a simulated frame rate of 30 fps. Figure 2 shows those Figure 3: Example of variations of x. Top: of the original video (at 25 fps), center: of the modified video (at 30 fps), bottom: modified, obtained by re-sampling the original one Robustness after adding logos Here, we deal with the robustness of the towards videos on which a logo has been superimposed. We have considered realistic cases for this experiment: the superimposition of the logo has been done on one of the frame s borders. Its position, in a corner for instance, can have an
7 influence on the result, and above all its content: a white logo will attract the centroid more than a black one. Even after this modification, the original video and the one with the logo should still be considered as identical by the comparison method. We have considered the detection of videos with logos using queries without, and vice-versa (Tab. 5). We obtained a precision of, in 90% of the cases, after adding a small logo, and in 60% of the cases for a medium one. As we can see, the performances tend to decrease when logos reach large sizes. Our method remains relatively sensitive to the addition of an element to the frame. A solution could be to consider only the central part of the frame during the computation of centroids. Size of logo Logo size compared with the frame size Nb of videos detected if the request has... a logo Small 0.95% 0/0 9/0 Medium 3.3% 5/0 5/0 no logo Table 5: Number of original videos (without logo) detected by copies with logos, and vice-versa. 4. CONCLUSION In this paper, we have proposed an efficient and robust video copy detection tool using a spatio-temporal. It has been developed in order to detect videos which have the same content as a video query. These videos are spotted by considering the distance between the four centroids of the request and those of the videos analyzed, on 200 consecutive images. Using a database of more than 0 hours of video, we have obtained a recall of for each of the 50 video extracts randomly drawn in the database, and the same precision in 92% of the cases. A good robustness to different transforms has also been observed, but a problem still remains concerning large logo superimposition. The size of the s is 72 Mb for the whole database, which is 730 times less than the videos themselves. The search only takes about 20 seconds on a 2 GHz Celeron processor. A future implementation, however, will accelerate this phase thanks to an intelligent scanning of the database. Combining audio fingerprinting to the current is also studied to improve results, as well as an improvement of the robustness against complex distortions of the signal, such as embedded videos. 5. REFERENCES [] S-C.S. Cheung and A. Zakhor, Estimation of web video multiplicity, in Proceedings of the SPIE - Internet Imaging, San Jose, California. January 22-28, 2000, pp [2] S-C.S. Cheung and A. Zakhor, Efficient video similarity measurement with video, IEEE Transactions on Circuits and Systems for Video Technology, vol. 3, no., pp , [3] A. Joly, C. Frelicot, and O. Buisson, Robust content-based video copy identification in a large reference database, in Conference on Image and Video Retrieval, CIVR 2003, Urbana-Champaign, IL, USA, July 24-25, 2003, pp [4] P. Indyk, G. Iyengar, and N. Shivakumar, Finding pirated video sequences on the internet, Tech. Rep., Computer Science Department, Stanford University, 999. [5] A. Hampapur, KH Hyun, and R. Bolle, Comparison of sequence matching techniques for video copy detection, in SPIE Conference on Storage and Retrieval for Media Databases, USA, January 20-25, [6] J. Oostveen, T. Kalder, and J. Haitsma, Feature extraction and a database strategy for video fingerprinting, in VISUAL 2002, Taipei, Taiwan, March -3, [7] T.C. Hoad and J. Zobel, Fast video matching with alignment, in Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, Berkeley, California, USA, November 7, [8] E. Allamanche, J. Herre, O. Hellmuth, B. Froba, T. Kastner, and M. Cremer, Content-based identification of audio material using mpeg-7 low level description, in Proceedings of the Second Annual International Symposium on Music Information Retrieval: ISMIR 200, Indiana, USA, October 5-7, 200. [9] P. Cano, E. Batlle, T. Kalker, and J. Haitsma, A review of algorithms for audio fingerprinting, in International Workshop on Multimedia Signal Processing, US Virgin Islands, December, [0] Trec video 2002,
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