Traditional saliency detection via absorbing Markov Chain can only detect the objects which have great contrast to the background or in the center of the images. However

in fact

the objects human focus on do not usually have these features. To alleviate this problem

propose an object-oriented saliency detection algorithm via absorbing Markov and apply it to saliency detection for monkeys’ faces. This algorithm introduces a penalty factor to traditional saliency detection via Markov Chain to dynamically adjust the absorbing times relying on some prior knowledge. The reward or punishment to the color weights is made according to the differences between the colors of the superpixel points and color information of objects to detect salient objects correctly. The experimental results demonstrate that comparing with traditional algorithm

the proposed algorithm can detect the objects which are focused on correctly

and especially when there are more than one object.