A site entropy rate and degree centrality based algorithm for image co-segmentation |
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| Affiliation: | 1. Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur 721302, India;2. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India;1. School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, Liaoning 116024, China;2. School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China;3. School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China;4. School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning 116024, China;1. School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi 530004, China;2. Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA;1. Department of Computer Science, Technion–Israel Institute of Technology, Haifa 32000, Israel;2. School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm 10044, Sweden |
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| Abstract: | In this paper, we propose a graph based algorithm that efficiently segments common objects from multiple images. We first generate a number of object proposals from each image. Then, an undirected graph is constructed based on proposal similarities and co-saliency maps. Two different methods are followed to extract the proposals containing common objects. They are: (1) degree centrality of nodes obtained after graph thresholding and (2) site entropy rate of nodes calculated on the stationary distribution of Markov chain constructed on the graph. Finally, we obtain the co-segmented image region by selecting the more salient of the object proposals obtained by the two methods, for each image. Multiple instances of the common object are also segmented efficiently. The proposed method has been compared with many existing co-segmentation methods on three standard co-segmentation datasets. Experimental results show its effectiveness in co-segmentation, with larger IoU values as compared to other co-segmentation methods. |
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| Keywords: | Site entropy rate Degree centrality Co-segmentation Co-saliency ?Markov chain Object proposal Stationary distribution |
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