Object-of-interest extraction based on sparse coding

Authors:
Xiaomeng Wang;Yanjiang Wang;Weifeng Liu;Hui Li
Affiliations:
China University of Petroleum (East China), Qingdao, P.R. China;China University of Petroleum (East China), Qingdao, P.R. China;China University of Petroleum (East China), Qingdao, P.R. China;China University of Petroleum (East China), Qingdao, P.R. China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

Usually, there are different kinds of targets in a complicated scene, but we human beings are only interested in some of them with salient or rare features, therefore how to detect and locate such objects of interest from a cluttered background is a key issue in computer vision research. In this paper, we propose an object-of-interest extraction method based on a rarity model derived from sparse coding. The rarity of an image is computed by analyzing the sparse coefficient matrix after dictionary learning and then used to extract the interested objects. Experimental results show that the proposed method has better performance than traditional methods.