A hierarchical graph model for object cosegmentation
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RESEARCH
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A hierarchical graph model for object cosegmentation Yanli Li, Zhong Zhou* and Wei Wu
Abstract Given a set of images containing similar objects, cosegmentation is a task of jointly segmenting the objects from the set of images, which has received increasing interests recently. To solve this problem, we present a novel method based on a hierarchical graph. The vertices of the hierarchical graph involve pixels, superpixels and heat sources, and cosegmentation is performed as iterative object refinement in the three levels. With the inter-image connection in the heat source level and the intra-image connection in the superpixel level, we progressively update the object likelihoods by transferring message across images via belief propagation, diffusing heat energy within individual image via random walks, and refining the foreground objects in the pixel level via guided filtering. Besides, a histogram based saliency detection scheme is employed for initialization. We demonstrate experimental evaluations with state-of-the-art methods over several public datasets. The results verify that our method achieves better segmentation quality as well as higher efficiency. Keywords: Cosegmentation, Hierarchical graph, Heat source, Saliency detection, Belief propagation, Random walks, Guided filtering
1 Introduction The term “cosegmentation” is first introduced by Rother et al. [1] in 2006, referring to the problem of simultaneously segmenting “similar” foreground objects in a set of images. The definition of “similar” commonly indicates the constraint that the distribution of some appearance cues such as color and texture in each image has to be similar. Cosegmentation has many potential applications. It can be used for summarizing personal photo album, guiding multiple images’ editing, boosting unsupervised object recognition, improving content based image retrieval and so on. Since the introduction of the problem, various methods have been presented. One type of methods handles the problem of multi-class cosegmentation, while others focus on binary cosegmentation. In this article, we are interested in binary cosegmentation and observe that for most applications of binary cosegmentation several criteria should be followed: (1) automation, i.e., it is executed without user interactions; (2) scalability, i.e., it can be applied to *Correspondence: [email protected] State Key Laboratory of Virtual Reality Technology & Systems, Beihang University, Beijing, China
hundreds of images instead of two images or small sized image sets; (3) focusing on “object” instead of “stuff ”. Here the “object” refers to “foreground things” such as a person or a bird, while “stuff ” refers to “background regions” such as road or sky; (4) high segmentation accuracy; (5) low running time. According to these criteria, existing methods have some limitations. For example, the iCoseg system presented by Batra et al. [2] can obtain highly accurate results, but requires user input. The methods reviewed by Vicente et al. [3] all foc
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