Title :
Salient object segmentation based on graph cut
Author :
Shi, Ran ; Liu, Zhi ; Xue, Yinzhu
Author_Institution :
Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China
Abstract :
Salient object segmentation is an important technique for many content based applications. This paper presents an unsupervised salient object segmentation method under the graph cut optimization framework. First, we exploit a kernel density estimation based saliency model to generate the saliency map, which provides the useful cues for object segmentation. Then we exploit the saliency map to adaptively define the region cost term, the boundary cost term and their balancing weight in the cost function, which is minimized using graph cut to obtain a binary segmentation of salient objects. Experimental results on a variety of images demonstrate the better segmentation performance of our approach.
Keywords :
content-based retrieval; graph theory; image retrieval; image segmentation; optimisation; binary segmentation; content-based image retrieval; graph cut optimization; kernel density estimation; unsupervised salient object segmentation method; Image segmentation;
Conference_Titel :
Intelligent Signal Processing and Communication Systems (ISPACS), 2010 International Symposium on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-7369-4
DOI :
10.1109/ISPACS.2010.5704679