Content similarity matching for video sequence identification
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To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.
Abstract
To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.
참고문헌 (11)
- H. Lu, "Hierarchical indexing structure for efficient similarity search in video retrieval", IEEE Trans. Knowledge and Data Engineering, 18(11), pp.1544~1559 (2006)
- M. Worring, "Semantic image and video indexing in broad domains", IEEE Trans. Multimedia, 9(5), pp.909~911 (2007)
- C. Snoek, "Multimedia Event-based video indexing using time intervals", IEEE Trans. Multimedia, 7(4), pp.638~647 (2005)
- V. T. Chasanis, "Scene detection in video using shot clustering and sequence alignment", IEEE Trans. Multimedia, 11(1), pp.89~100 (2009)
- C. Cotsaces, "Face-based digital signatures for video retrieval", IEEE Trans. Circuits and Systems for Video Technology, 18(4), pp.549~533 (2008)
- D. P. Mukherjee, "Key frame estimation in video using randomness measure of feature point pattern", IEEE Trans. Circuits and Systems for Video Technology, 17(5), pp.612~620 (2007)
- J. Xu, "Temporal segmentation of 3-D video by histogram-based feature vectors", IEEE Trans. Circuits and Systems for Video Technology, 19(6), pp.870~881 (2009)
- N. Sebe, "Toward improved ranking metrics", IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-22, pp.1132~1143 (2000)
- Z.-Q. Zhou, "A modified Hausdorff distance using edge gradient for robust object matching", Proc. Int. Conf. Image Analysis and Signal Processing, pp.250~254 (2009)
- S. Chu, "Environmental sound recognition with time-frequency audio features", IEEE Trans. Audio, Speech, and Language Processing, pp.1142~1158 (2009)
- B. Lui, "Ontology based content management for digital television services", Proc. IEEE Int. Conf. e-Business Engineering, pp.565~570 (2009)