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17 January 2005 Boosting the discriminative power of color models for feature detection
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We consider the well-known problem of segmenting a color image into foreground-background pixels. Such result can be obtained by segmenting the red, green and blue channels directly. Alternatively, the result may be obtained through the transformation of the color image into other color spaces, such as HSV or normalized colors. The problem then is how to select the color space or color channel that produces the best segmentation result. Furthermore, if more than one channels are equally good candidates, the next problem is how to combine the results. In this article, we investigate if the principles of the formal model for diversification of Markowitz (1952) can be applied to solve the problem. We verify, in theory and in practice, that the proposed diversification model can be applied effectively to determine the most appropriate combination of color spaces for the application at hand.
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Harro M. G. Stokman and Theo Gevers "Boosting the discriminative power of color models for feature detection", Proc. SPIE 5670, Internet Imaging VI, (17 January 2005);


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