NDLI: A Hybrid Graph Model for Unsupervised Object Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Guangcan Liu Zhouchen Lin Xiaoou Tang Yong Yu
Copyright Year	2007
Description	Author affiliation: Shanghai Jiao Tong Univ., Shanghai (Guangcan Liu)
Abstract	In this work, we address the problem of performing class specific unsupervised object segmentation, i.e., automatic segmentation without annotated training images. We propose a hybrid graph model (HGM) to integrate recognition and segmentation into a unified process. The vertices of a hybrid graph represent the entities associated to the object class or local image features. The vertices are connected by directed edges and/or undirected ones, which represent the dependence between the shape priors of the class (for recognition) and the similarity between the color/texture priors within an image (for segmentation), respectively. By simultaneously considering the Markov chain formed by the directed subgraph and the minimal cut of the undirected subgraph, the likelihood that the vertices belong to the underlying class can be computed. Given a set of images each containing objects of the same class, our HGM based method automatically identifies in each image the area that the objects occupy. Experiments on 14 sets of images show promising results.
Starting Page	1
Ending Page	8
File Size	1371424
Page Count	8
File Format	PDF
ISBN	9781424416301
ISSN	15505499
DOI	10.1109/ICCV.2007.4408962
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object segmentation Shape Image segmentation Object recognition Asia Image recognition Computer vision Humans Text analysis Image analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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