Abstract
In this work, we have presented a video database indexing methodology that works well for a content based video copy detection (CBVCD) system. Video data is first segmented into cohesive units called shots. A clustering based method is proposed to extract one or more Representative frames from the shots. On such collection of representatives extracted from all the shots in the video database, triangle inequality based image database indexing scheme is applied. Thus, video indexing is mapped to the task of image indexing. For a shot, following the proposed methodology primarily candidate shots corresponding to the matched representative frames are retrieved. Only on such small number of candidates the rigorous video sequence matching technique can be applied to make final decision by the CBVCD system or video retrieval system. Experimental result with a CBVCD system indicates significant gain in terms of speed, reduces false alarm rate without much compromise in terms of correct recognition rate in comparison to exhaustive search.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brunelli, R., Mich, O., Moden, C.M.: A survey on the automatic indexing of video data. Journal of Visual Communication and Image Representation 10, 78–112 (1999)
Smeaton, A.F.: Techniques used and open challenges to the analysis, indexing and retrieval of digital video. Information Systems 32, 545–559 (2007)
Zhang, H.J., Wu, J., Zhong, D., Smoliar, S.W.: An integrated system for content based video retrieval and browsing. Pattern Recognition 30(4), 643–658 (1997)
Bertini, M., Bimbo, A.D., Pala, P.: Indexing for reuse of tv news shot. Pattern Recognition 35, 581–591 (2002)
Li, J.Z., OZsu, M.T., Szafron, D.: Modeling video temporal relationships in an object database systems. In: Proc. SPIE Multimedia Computing and Networking, pp. 80–91 (1997)
Pingali, G., Opalach, A., Jean, Y., Carlbom, I.: Instantly indexed multimedia databases of real world events. IEEE Trans. on Multimedia 4(2), 269–282 (2002)
Ren, W., Singh, S., Singh, M., Zhu, Y.S.: State-of-the on spatio-temporal information-based video retrieval. Pattern Recognition 42 (2009)
Ren, W., Singh, S.: Video sequence matching with spatio-temporal constraint. In: Intl. Conf. Pattern Recog., pp. 834–837 (2004)
Fablet, R., Bouthmey, P.: Motion recognition using spatio-temporal random walks in sequence of 2d motion-related measurements. In: Proc. Intl. Conf. on Image Processing, pp. 652–655 (2001)
Fleuret, F., Berclaz, J., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. on PAMI 20(2), 267–282 (2008)
Fu Ai, L., Qing Yu, J., Feng He, Y., Guan, T.: High-dimensional indexing technologies for large scale content-based image retrieval: A review. Journal of Zhejiang University-SCIENCE C (Computers & Electronics) 14(7), 505–520 (2013)
Zhou, L.: Research on local features aggregating and indexing algorithm in large-scale image retrieval. Master Thesis, Huazhong University of Science and Technology, China 10–15 (2011)
Robinson, T.J.: The k-d-b tree: A search structure for large multidimensional dynamic indexes. In: Proc. ACM SIGMOD Intl. Conf. on Management of Data, pp. 10–18 (1981)
Skopal, T., Lokoc, J.: New dynamic construction techniques for m-tree. Journal of Discrete Algorithm 7(1), 62–77 (2009)
Lin, K.I., Jagadish, H.V., Faloutsos, C.: The tv-tree: An index structure for high-dimensional data. VLDB Journal 3(4), 517–542 (1994)
Zhuang, Y., Liu, Y., Wu, F., Zhang, Y., Shao, J.: Hypergraph spectral hashing for similarity search of social image. In: Proc. ACM Int. Conf. on Multimedia, pp. 1457–1460 (2011)
Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon, S.E.: Spherical hashing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2957–2964 (2012)
Avrithis, Y., Kalantidis, Y.: Approximate gaussian mixtures for large scale vocabularies. In: Proc. European Conf. on Computer Vision, pp. 15–28 (2012)
Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. PAMI 33(1), 117–128 (2011)
Dutta, D., Saha, S.K., Chanda, B.: An attack invariant scheme for content-based video copy detection. Signal Image and Video Processing 7(4), 665–677 (2013)
Mohanta, P.P., Saha, S.K., Chanda, B.: A model-based shot boundary detection technique using frame transition parameters. IEEE Trans. on Multimedia 14(1), 223–233 (2012)
Berman, A.P., Shapiro, L.G.: A flexible image database system for content-based retrieval. Computer Vision and Image Understanding 75(1/2), 175–195 (1999)
Ciocca, G., Schettini, R.: An innovative algorithm for key frame extraction in video summarization. Real Time IP 1, 69–98 (2006)
Mohanta, P.P., Saha, S.K., Chanda, B.: A novel technique for size constrained video storyboard generation using statistical run test and spanning tree. Int. J. Image Graphics 13(1) (2013)
Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. Journal of Cybernetica 4, 95–104 (1974)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Dutta, D., Saha, S.K., Chanda, B. (2014). Indexing Video Database for a CBVCD System. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-07353-8_36
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07352-1
Online ISBN: 978-3-319-07353-8
eBook Packages: EngineeringEngineering (R0)