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Computation ally Efficient Video Object Segmentation using SOM-Based Hierarchical Clustering  

Jung Chan-Ho (Digital TV Research Lab. LG Electronics)
Kim Gyeong-Hwan (Department of Electronic Engineering, Sogang University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.43, no.4, 2006 , pp. 74-86 More about this Journal
Abstract
This paper proposes a robust and computationally efficient algorithm for automatic video object segmentation. For implementing the spatio-temporal segmentation, which aims for efficient combination of the motion segmentation and the color segmentation, an SOM-based hierarchical clustering method in which the segmentation process is regarded as clustering of feature vectors is employed. As results, problems of high computational complexity which required for obtaining exact segmentation results in conventional video object segmentation methods, and the performance degradation due to noise are significantly reduced. A measure of motion vector reliability which employs MRF-based MAP estimation scheme has been introduced to minimize the influence from the motion estimation error. In addition, a noise elimination scheme based on the motion reliability histogram and a clustering validity index for automatically identifying the number of objects in the scene have been applied. A cross projection method for effective object tracking and a dynamic memory to maintain temporal coherency have been introduced as well. A set of experiments has been conducted over several video sequences to evaluate the proposed algorithm, and the efficiency in terms of computational complexity, robustness from noise, and higher segmentation accuracy of the proposed algorithm have been proved.
Keywords
video object segmentation; self-organizing map(SOM); hierarchical clustering; motion vector reliability; rule-based region processing;
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1 S. H. Kwok, A. G. Constantinides, and W.-c. Siu, 'An efficient recursive shortest spanning tree algorithm using linking properties,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 14, pp. 852-863, 2004   DOI   ScienceOn
2 M. Y. Kiang, 'Extending the Kohonen selforganizing map networks for clustering analysis,' Comp. Stat. & Data Anal., vol. 38, pp. 161-180, 2001   DOI   ScienceOn
3 P. Xu, C.-H. Chang, and A. Paplinski, 'Self-organizing topological tree for online vector quantization and data clustering,' IEEE Trans. on Sys., Man and Cyb., vol. 35, pp. 515-526, 2005   DOI   ScienceOn
4 Y. Tsaig and A Averbuch, 'Automatic segmentation of moving objects in video sequences: a region labeling approach,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 12, pp. 597-612, 2002   DOI   ScienceOn
5 I. Patras, E. A. Hendriks, and R. L. Lagendijk, 'Confidence measures for block matching motion estimation,' IEEE Int. Conf. on Imag. Proc, vol. 2, pp. 277-280, 2002   DOI
6 R. Neher and A. Srivastava, 'A Bayesian MRF framework for labeling terrain using hyperspectral imaging,' IEEE Trans. on Ceo. and Rem Sen, vol. 43, pp. 1363-1374, 2005   DOI   ScienceOn
7 O. J. Morris, J. M. Lee, A. G. Constantinides, 'A unified method for segmentation and edge detection using graph theory,' IEEE Int Conf. on Acou, Spe., and Sig. Proc., vol. 11, pp. 2051-2055, 1986   DOI
8 U. Maulik and S. Bandyopadhyay, 'Performance evaluation of some clustering algorithms and validity indices,' IEEE Trans. on Pat. Anal. and Mach Intel., vol. 24, pp. 1650-1654, 2002   DOI   ScienceOn
9 F. Dufaux and J. Konrad, 'Efficient, robust, and fast global motion estimation for video coding,' IEEE Trans. on Imag. Proc., vol. 9, pp. 497-501, 2000   DOI   ScienceOn
10 Y. Su, M.-T. Sun, and V. Hsu, 'Global motion estimation from coarsely sampled motion vector field and the applications,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 15, pp. 232-242, 2005   DOI   ScienceOn
11 R. Cucchiara, A Prati, and R Vezzani, 'Real-time motion segmentation from moving cameras,' Real-Time Imag., vol. 10, pp. 127-143, 2004   DOI   ScienceOn
12 J. Vesanto and E. Alhoniemi, 'Clustering of the self-organizing map,' IEEE Trans. on Neu. Net. vol. 11, pp. 586-600, 2000   DOI   ScienceOn
13 S. Wu and T. W. S. Chow, 'Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density,' Pat Rec., vol. 37, pp. 175-188, 2004   DOI   ScienceOn
14 D. Wang, 'Unsupervised video segmentation based on watersheds and temporal tracking,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 8, pp. 539-546, 1998   DOI   ScienceOn
15 J. Y. A. Wang and E H Adelson, 'Representing moving images with layers,' IEEE Trans. on Image. Proc, vol. 3, pp. 625-638, 1994   DOI   ScienceOn
16 A. A. Alatan, L. Onural, M. Wollborn, R. Mech, E Tuncel, and T. Sikora, 'Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 8, pp. 802-813, 1998   DOI   ScienceOn
17 E. Tuncel and L. Onural, 'Utilization of the recursive shortest spanning tree algorithm for video-object segmentation by 2- D affine motion modeling,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 10, pp. 776-781, 2000   DOI   ScienceOn
18 V. Mezaris, I. Kompatsiaris, and M. G. Strintzis, 'Video object segmentation using Bayes-based temporal tracking and trajectory-based region merging,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 14, pp. 782-795, 2004   DOI   ScienceOn
19 H. Xu, A. Younix, and M. Kabuka, 'Automatic moving object extraction for content-based applications,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 14, pp. 796-812, 2004   DOI   ScienceOn
20 M. Kim, J. G. Choi, D. Kim, H. Lee, M. H. Lee, C. Ahn, and Y-S. Ho, 'A VOP generation tool: automatic segmentation of moving objects in image sequences based on spatio-ternporal information,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 9, pp. 1216-1226, 1999   DOI   ScienceOn
21 T. Meier and K. N. Ngan, 'Video segmentation for content-based coding,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 9, pp. 1190-1203, 1999   DOI   ScienceOn
22 Y. Liu and Y. F. Zheng , 'Video object segmentation and tracking using ${\psi}-learning$ classification,' IEEE Trans. on Cir. and Sys. for Video Tech, vol. 15, pp. 885-899, 2005   DOI   ScienceOn
23 A. D. Doulamis, N. Doulamis, and S. Kallas, 'Non-sequential video content representation using temporal variation of feature vectors,' IEEE Trans. on Cons. Elec., vol. 46, pp. 758-768, 2000   DOI   ScienceOn
24 J. Kim and T. Chen, 'Multiple feature clustering for image sequence segmentation,' Pat. Rec. Let., vol. 22, pp. 1207-1217, 2001   DOI   ScienceOn
25 D. W. Murray and B. F. Buxton, 'Scene segmentation from visual motion using global optimization,' IEEE Trans. on Pat Anal. and Mach Intel., vol. 9, pp. 220-228, 1987   DOI
26 A. Doulamis, N. Doulamis, K. Ntalianis, and S. Kollias, 'An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture,' IEEE Trans. on Neu Net, vol. 14, pp. 616-630, 2003   DOI   ScienceOn