Learning probabilistic structure to group image edges for object extraction

Authors:
Yangyu Tao;Lin Liang;Yingqing Xu
Affiliations:
Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, MOE, Hefei, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

We investigate exploiting the class specific information in the conventional perceptual edge grouping for the task of object extraction, since the domain information is usually available in practice. Instead of applying the classical Gestalt principles, we turn to learn a class specific probabilistic structure model from training images. During the learning, both geometrical and photometric features such as color and texture are fused. Experiments show the model is fairly robust to the intra-class variations of object as well as background clutters. Moreover, we design a novel saliency measure for the grouping based on the probabilistic structure model. The object extraction is formulated as an optimization problem which can be efficiently solved by the recently developed ratio contour algorithm. The effectiveness of the proposed method is demonstrated by the experiments on real images.