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. Th...

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Bibliographic Details
Published in2008 IEEE Conference on Computer Vision and Pattern Recognition pp. 1 - 8
Main Authors Jianguo Li, Weixin Wu, Tao Wang, Yimin Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Histograms
Image analysis
Image edge detection
Image representation
Image resolution
Image retrieval
Machine vision
Pattern analysis
Robustness
Spatial resolution
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ISBN9781424422425
1424422426
ISSN1063-6919
1063-6919
DOI10.1109/CVPR.2008.4587839

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Summary: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.
ISBN:9781424422425
1424422426
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2008.4587839