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The estimation of camera motion is one of the most important aspects for video processing, analysis, indexing, and retrieval. Most of existing techniques to estimate camera motion are based on optical flow methods in the uncompressed domain. However, to decode and to analyze a video sequence is extremely time-consuming. Since video data are usually available in MPEG-compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for estimating camera motion in MPEG video sequences. Our technique relies on linear combinations of optical flow models. The proposed method first creates prototypes of optical flow, and then performs a linear decomposition on the MPEG motion vectors, which is used to estimate the camera parameters. Experiments on synthesized and realworld video clips show that our technique is more effective than the state-of-the-art approaches for estimating camera motion in MPEG video sequences.

Comparison Of Video Sequences With Histograms Of Motion Patterns

Making efficient use of video information requires the development of a video signature and a similarity measure to rapidly identify similar videos in a huge database. Most of existing techniques to address this problem have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are extremely time-consuming tasks. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for comparing video sequences that works in the compressed domain. The proposed method is based on recognizing motion patterns extracted from the video stream and their occurrence histogram is proven to be a powerful feature for describing the video content. Experiments on a TRECVID 2010 dataset show that our approach presents high accuracy relative to the state-of-the-art solutions and in a computational time that makes it suitable for large collections. © 2011 IEEE.36733676IEEE,IEEE Signal Processing SocietyChen, L., Stentiford, F.W.M., Video sequence matching based on temporal ordinal measurement (2008) Pattern Recog. Lett., 29 (13), pp. 1824-1831Hampapur, A., Bolle, R.M., Comparison of distance measures for video copy detection (2001) ICME, pp. 737-740Hua, X.-S., Chen, X., Zhang, H., Robust video signature based on ordinal measure (2004) ICIP, pp. 685-688Kim, C., Vasudev, B., Spatiotemporal sequence matching for efficient video copy detection (2005) IEEE Transactions on Circuits and Systems for Video Technology, 15 (1), pp. 127-132. , DOI 10.1109/TCSVT.2004.836751Law-To, J., Chen, L., Joly, A., Laptev, I., Buisson, O., Gouet-Brunet, V., Boujemaa, N., Stentiford, F., Video copy detection: A comparative study (2007) CIVR, pp. 371-378Hampapur, A., Bolle, R.M., Feature based indexing for media tracking (2000) ICME, pp. 1709-1712Almeida, J., Valle, E., Torres, R.S., Leite, N.J., DAHC-tree: An effective index for approximate search in high-dimensional metric spaces (2010) J. Inf. Data Management, 1 (3), pp. 375-390Almeida, J., Torres, R.S., Leite, N.J., BP-tree: An efficient index for similarity search in high-dimensional metric spaces (2010) CIKM, pp. 1365-1368Almeida, J., Minetto, R., Almeida, T.A., Torres, R.S., Leite, N.J., Robust estimation of camera motion using optical flow models (2009) ISVC, pp. 435-446Almeida, J., Minetto, R., Almeida, T.A., Torres, R.S., Leite, N.J., Estimation of camera parameters in video sequences with a large amount of scene motion (2010) IWSSIP, pp. 348-35

Rapid Cut Detection On Compressed Video

The temporal segmentation of a video sequence is one of the most important aspects for video processing, analysis, indexing, and retrieval. Most of existing techniques to address the problem of identifying the boundary between consecutive shots have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are two extremely time-consuming tasks. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for video cut detection that works in the compressed domain. The proposed method is based on both exploiting visual features extracted from the video stream and on using a simple and fast algorithm to detect the video transitions. Experiments on a real-world video dataset with several genres show that our approach presents high accuracy relative to the state-of-the-art solutions and in a computational time that makes it suitable for online usage. © 2011 Springer-Verlag.7042 LNCS7178Universidad de La Frontera (UFRO),The International Association for Pattern Recognition (IAPR),Asociacon Chilena de Reconocimiento de Patrones (AChiRP),Asociacion Cubana de Reconocimiento de Patrones (ACPR),Mex. Assoc. Comput. Vis., Neural Comput. Rob. (MACVNR)Almeida, J., Leite, N.J., Torres, R.S., Comparison of video sequences with histograms of motion patterns (2011) Int. Conf. Image Processing (ICIP 2011)Almeida, J., Minetto, R., Almeida, T.A., Torres, R.S., Leite, N.J., Robust estimation of camera motion using optical flow models (2009) LNCS, 5875, pp. 435-446. , Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. Springer, HeidelbergAlmeida, J., Minetto, R., Almeida, T.A., Torres, R.S., Leite, N.J., Estimation of camera parameters in video sequences with a large amount of scene motion (2010) Proc. of Int. Conf. Syst. Signals Image (IWSSIP 2010), pp. 348-351Almeida, J., Rocha, A., Torres, R.S., Goldenstein, S., Making colors worth more than a thousand words (2008) Int. Symp. Applied Comput. (ACM SAC 2008), pp. 1180-1186Bezerra, F.N., Leite, N.J., Using string matching to detect video transitions (2007) Pattern Anal. Appl., 10 (1), pp. 45-54Bouch, A., Kuchinsky, A., Bhatti, N.T., Quality is in the eye of the beholder: Meeting users' requirements for internet quality of service (2000) Int. Conf. Human Factors Comput. Syst. (CHI 2000), pp. 297-304Guimarães, S.J.F., Patrocínio Jr., Z.K.G., Paula, H.B., Silva, H.B., A new dissimilarity measure for cut detection using bipartite graph matching (2009) Int. J. Semantic Computing, 3 (2), pp. 155-181Hanjalic, A., Shot-boundary detection: Unraveled and resolved? (2002) IEEE Trans. Circuits Syst. Video Techn., 12 (2), pp. 90-105Koprinska, I., Carrato, S., Temporal video segmentation: A survey (2001) Signal Processing: Image Communication, 16 (5), pp. 477-500Lee, S.W., Kim, Y.M., Choi, S.W., Fast scene change detection using direct feature extraction from MPEG compressed videos (2000) IEEE Trans. Multimedia, 2 (4), pp. 240-254Lienhart, R., Reliable transition detection in videos: A survey and practitioner's guide (2001) Int. J. Image Graphics, 1 (3), pp. 469-486Pei, S.C., Chou, Y.Z., Efficient MPEG compressed video analysis using macroblock type information (1999) IEEE Trans. Multimedia, 1 (4), pp. 321-333Pfeiffer, S., Lienhart, R., Kühne, G., Effelsberg, W., The MoCA project - Movie content analysis research at the University of Mannheim (1998) GI Jahrestagung, pp. 329-338Whitehead, A., Bose, P., Laganière, R., Feature based cut detection with automatic threshold selection (2004) LNCS, 3115, pp. 410-418. , Enser, P.G.B., Kompatsiaris, Y., O'Connor, N.E., Smeaton, A., Smeulders, A.W.M. (eds.) CIVR 2004. Springer, HeidelbergYeo, B.L., Liu, B., Rapid scene analysis on compressed video (1995) IEEE Trans. Circuits Syst. Video Techn., 5 (6), pp. 533-544Zhang, H., Kankanhalli, A., Smoliar, S.W., Automatic partitioning of full-motion video (1993) Multimedia Syst., 1 (1), pp. 10-2

Reusing A Compound-based Infrastructure For Searching Video Stories

The fast evolution of technology has led to a growing demand for multimedia data, increasing the amount of research into efficient systems to manage those materials. A lot of research has being done by the Content-Based Image Retrieval (CBIR) community in the field of images. Nowadays, they play a key role in digital applications. Thus, contextual integration of images with different sources is vital. It involves reusing and aggregating a large amount of information with other media types. In particular, if we consider video data, images can be used to summarize videos into storyboards, providing an easy way to navigate and to browse large video collections. This has been the goal of a quickly evolving research area known as video summarization. In this paper, we present a novel approach to reuse the CBIR infrastructure for searching video stories, taking advantage of the compound object (CO) concept to integrate resources. Our approach relies on a specific component technology to encapsulate the CBIR related tasks and integrate them with video summarization techniques, known as Digital Content Component (DCC). Such a strategy provides an effective way to reuse, compose, and aggregate both content and processing software. © 2011 IEEE.222227 IEEE Systems, Man and Cybernetics Society (SMC),Society for Information Reuse and Integration (SIRI)Achananuparp, P., McCain, K.W., Allen, R.B., Supporting student collaboration for image indexing (2007) Int. Conf. Asia-Pacific Digital Libraries (ICADL'07), pp. 24-34Almeida, J., Minetto, R., Almeida, T.A., Torres, R.S., Leite, N.J., Robust estimation of camera motion using optical flow models (2009) Int. Symp. Advances VisualComput. (ISVC'09), pp. 435-446Almeida, J., Minetto, R., Almeida, T.A., Torres, R.S., Leite, N.J., Estimation of camera parameters in video sequences with a large amount of scene motion (2010) IEEE Int. Conf. Syst. Signals and Image Proc. (IWSSIP'10), pp. 348-351Almeida, J., Pinto-Ćaceres, S.M., Torres, R.S., Leite, N.J., Intuitive video browsing along hierarchical trees (2011) Technical Report IC-11-06, , Institute ofComputing, University of CampinasAlmeida, J., Torres, R.S., Leite, N.J., Rapid video summarization oncompressed video (2010) IEEE Int. Symp. Multimedia (ISM'10), pp. 113-120Avila, S.E.F., Lopes, A.P.B., Luz Jr., A., Aráujo, A.A., VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method (2011) Pattern Recognition Letters, 32 (1), pp. 56-68Benini, S., Migliorati, P., Leonardi, R., Retrieval of video story units by markov entropy rate (2008) IEEE Int. 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Conf. on Research and Advanced Techn. for Digital Libraries (ECDL'10), pp. 1-8Lagoze, C., De Sompel, H.V., Compound information objects: The OAI-ORE perspective (2007) Open Archives Initiative Object Reuse and Exchange White Paper, , http://www.openarchives.org/ore/documentsMundur, P., Rao, Y., Yesha, Y., Keyframe-based video summarization using Delaunay clustering (2006) International Journal on Digital Libraries, 6 (2), pp. 219-232. , DOI 10.1007/s00799-005-0129-9Murthy, U., Fox, E.A., Chen, Y., Hallerman, E., Torres, R.S., Ramos, E.J., Falc̃ao, T.R.C., Superimposed image description and retrieval for fish species identification (2009) Euro. Conf. Research Advanced Techn. 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