Abstract:
Aiming at the problems of traditional saliency detection algorithm, such as insufficient luminance, inaccurate saliency and background noise, we propose a saliency detection algorithm via convex hull calculation and color features. Considering that an image has different color value ranges in different color spaces, we firstly obtain a region contrast map by super-pixel segmentation in multi-color space. Then, a smooth channel difference map is obtained in the CIELAB space. Next, the color boosted Harris method is used to form a convex hull to obtain a center prior map and a convex hull structure map. The final saliency map is obtained by fusing four saliency map and optimizing them. This algorithm is more similar to the result of the artificial marked graph, it can not only separate the salient object from the background, restrain the background interference and highlight the salient region, but also obtain an full-resolution of the salient map. The proposed algorithm is compared with other 8 existing saliency detection algorithms on the public image dataset. Experimental results show that the algorithm is better than other algorithms.