Graph Presentation Random Walk Salient Object Detection Algorithm Based on Global Isolation and Local Homogeneity
Hu Zheng · Acta Automatica Sinica · 2011
The existing saliency detection algorithm mainly focuses on the inter-pixel contrast and lacks global perspective for analyzing and understanding the object in complex surroundings.According to the thought that a salient object in an image is often conspicuous and compact,an unsupervised graph presentation random walk salient object extraction algorithm based on global isolation and local homogeneity is proposed,and the problem of salient region detection is formulated as Markov random walk.First of all,the graph model is formed by dividing the input image into block images and using color and orientation features to determine the weight of edge,and then the isolated regions are obtained by using the random walk on a complete graph to extract the global properties of the image.Meanwhile,the uniform regions are enhanced by using the random walk on a k-regular graph to extract the local properties of the image.Finally,the saliency map is obtained by combining the global properties and local properties of the image,and the salient object is located and extracted according to the saliency map.Experimental results show that the proposed algorithm is more reasonable and effective than the two representative methods for salient object detection.