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Dynamic texture segmentation using spectral clustering based on CHMMs
Author(s): Yulong Qiao; Qiufei Liu; Kejun Wu; Jinhui Sheng; Qiuxia Liu; Na Li
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Paper Abstract

In this paper, we introduce the spectral clustering method based on Continuous Hidden Markov Model (CHMM) into dynamic texture (DT) segmentation. In order to characterize the DT, CHMMs are used to model all spatial subblocks of the DT. The initial segmentation is realized by utilizing the spectral clustering based on CHMMs. The similarity between two different CHMMs is measured with approximated Kullback-Leibler divergence (KLD). To improve the DT segmentation performance, the mathematical morphology method is also applied into further processing which is operated on the pixel level. Experimental results on artificially synthesized DT samples of DynTex dataset demonstrate the effectiveness of the proposed method.

Paper Details

Date Published: 9 August 2018
PDF: 6 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062Y (9 August 2018); doi: 10.1117/12.2503326
Show Author Affiliations
Yulong Qiao, Harbin Engineering Univ. (China)
Qiufei Liu, Harbin Engineering Univ. (China)
Kejun Wu, Harbin Engineering Univ. (China)
Jinhui Sheng, Harbin Engineering Univ. (China)
Qiuxia Liu, Zhoukou Normal Univ. (China)
Na Li, Harbin Engineering Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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