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- W947713053 abstract "An efficient method using various histogram-based (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 eigenspaees. 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." @default.
- W947713053 created "2016-06-24" @default.
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- W947713053 date "2003-01-01" @default.
- W947713053 modified "2023-09-27" @default.
- W947713053 title "Color Image Classification Using Support Vector Machines" @default.
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