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One step beyond histograms: Image representation using Markov stationary features

10 years 3 months ago
One step beyond histograms: Image representation using Markov stationary features
This paper proposes a general framework called Markov stationary features (MSF) to extend histogram based features. The MSF characterizes the spatial co-occurrence of histogram patterns by Markov chain models, and finally yields a compact feature representation through Markov stationary analysis. Therefore, the MSF goes one step beyond histograms since it now involves spatial structure information of both within histogram bins and between histogram bins. Moreover, it still keeps simplicity, compactness, efficiency, and robustness. We demonstrate how the MSF is used to extend histogram based features like color histogram, edge histogram, local binary pattern histogram and histogram of oriented gradients. We evaluate the MSF extended histogram features on the task of TRECVID video concept detection. Results show that the proposed MSF extensions can achieve significant performance improvement over corresponding histogram features.
Jianguo Li, Weixin Wu, Tao Wang, Yimin Zhang
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Jianguo Li, Weixin Wu, Tao Wang, Yimin Zhang
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