Abstract
This paper is concerned with selective color feature for detecting salient regions. In contrast to most existing methods related to detection in one color space, the proposed algorithm pre-segments the input image into superpixels in both RGBY color space and Lab color space. Next, to calculate color contrast we not only consider the local feature, but also compute the difference between the pixel and the whole image. In the meanwhile, based on the center-surrounding scheme, a new computational model of color distribution features is presented to detect salient regions. Finally, 2D entropy is employed as an evaluation criterion to select and integrate the proper color features. Experimental results show that the proposed method outperforms the state-of-the-art methods on salient region detection.
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