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2003
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Color photo categorization using compressed histograms and support vector machines

9 years 11 months ago
Color photo categorization using compressed histograms and support vector machines
In this paper, an efficient method using various histogrambased (high-dimensional) image content descriptors for automatically classifying general color photos into relevant categories is presented. Principal component analysis (PCA) is used to project the original high dimensional histograms onto their eigenspaces. Lower dimensional eigenfeatures are then used to train support vector machines (SVMs) to classify images into their categories. Experimental results show that even though different descriptors perform differently, they are all highly redundant. It is shown that the dimensionality of all these descriptors, regardless of their performances, can be significantly reduced without affecting classification accuracy. Such scheme would be useful when it is used in an interactive setting for relevant feedback in content-based image retrieval, where low dimensional content descriptors will enable fast online learning and reclassification of results.
Xia Feng, Jianzhong Fang, Guoping Qiu
Added 24 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2003
Where ICIP
Authors Xia Feng, Jianzhong Fang, Guoping Qiu
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