Abstract
The conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin, thus it is sensitive to noisy interference such as illumination changes. We propose a new concept of color histogram representation, called fuzzy color histogram (FCH), to address the above mentioned issue by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function, individually. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. In addition, in contrast with quadratic histogram distance, our method shifts the computation load from on-line retrieval to off-line indexing. Such computing methodology is fairly desirable for image retrieval over large image databases.
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