Salient object detection algorithm based on space distribution and local complexity
Meng Peng-quan · Journal of Yanshan University · 2010
The current existing salient region detection algorithm mainly focus on the inter-pixel contrast and lack of the analysis and comprehension of salient object features from the global perspective.According to the thought that a salient object in a image is often compact and complete,a unsupervised salient object extraction algorithm based on space distribution and local complexity is proposed in this paper.First of all,the intensity saliency map is obtained by computing the contrast of local area and its neighborhood region,and then the color saliency map is computed using conspicuous,space distribution and regional homogeneity of color information.Meanwhile,the orientation saliency map is obtained by multi-scale analysis of the spatial distribution and regional complexity of the orientation.Finally,a integrated saliency map of the input image is composed through fusion strategy by combining the spatial distribution of salient value with enhancement factor of area,and the object of intere st is located and extracted according to the saliency map.The proposed method is applied to all kinds of color images,and the better test results show that the algorithm is feasible and valuable.