Saliency Detection Model Based on Region Contrast of Color and Orientation
Pang Jing-cha · 2014
Saliency detection is an important step in many computer vision tasks.A region contrast saliency estimation algorithm based on color and orientation contrast of the segmented regions is proposed.First of all,the input image is segmented into regions using the algorithm of graph-based image segmentation,then define the color saliency for each region as the weighted sum of the region's contrasts to all other regions in the image.Meanwhile,we segment the image into regions using the algorithm of texture segmentation,then compute the orientation saliency for each region as the weighted sum of the region's contrasts to all other regions in the image.Finally,the saliency map of the input image is obtained by combining color saliency map and orientation saliency map,so the salient object is located and extracted.Our algorithm outperformed existing saliency detection methods when evaluated using the publicly available data sets.The results show that the proposed algorithm is more reasonable and effective.