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Modeling human color categorization
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DOI：10.1016/j.patrec.2007.09.006.png)
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Abstract
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A unique color space segmentation method is introduced. It is founded on features of human cognition, where 11 color categories are used in processing color. In two experiments, human subjects were asked to categorize color stimuli into these 11 color categories, which resulted in markers for a Color LookUp Table (CLUT). These CLUT markers are projected on two 2D projections of the HSI color space. By applying the newly developed Fast Exact Euclidean Distance (FEED) transform on the projections, a complete and efficient segmentation of color space is achieved. With that, a human-based color space segmentation is generated, which is invariant for intensity changes. Moreover, the efficiency of the procedure facilitates the generation of adaptable, application-centered, color quantization schemes. It is shown to work excellently for color analysis, texture analysis, and for Color-Based Image Retrieval purposes. (C) 2007 Elsevier B.V. All rights reserved.
Keywords:
eleven color categories
human color categorization
color space segmentation
Fast Exact Euclidean Distance (FEED) transform
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