It’s a new idea to make computers be able to obtain “sensations” from a color image through some unsupervised ways. To let the idea come into true, a granule-based model, based on granular computing(GrC) which is a new way to simulate human thinking to help solve complicated problems in the field of computational intelligence, is proposed for color image processing. First, this paper deems data a hypercube, defines two new concepts, attribute granules(AtG) and connected granules(CoG), and presents the definitions of the granule-based model. Then, in order to fulfill the granule-based model, this paper designs a single attribute analyser(SAA), defines some theorems and lemmas related to decomposition, and describes the processing of extracting all attibute granules. Experimental results on over 300 color images show that the proposed analyser is accurate, robust, high-speed, and able to provide computers with “sensations”.
Color is one of the important characteristics in visual perception. But color histograms used commonly loses spatial distribution information while obtains the statistical feature of image color. This paper provides an algorithm, the region partitioning algorithm based on concentric circles, that synthesizes the color, texture and space information to extract features of image. It evidently improves the precision of retrieval and achieve better performance.
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