NDLI: Online Glocal Transfer for Automatic Figure-Ground Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Wenbin Zou Cong Bai Kpalma, K. Ronsin, J.
Copyright Year	1992
Abstract	This paper addresses the problem of automatic figure-ground segmentation, which aims at automatically segmenting out all foreground objects from background. The underlying idea of this approach is to transfer segmentation masks of globally and locally (glocally) similar exemplars into the query image. For this purpose, we propose a novel high-level image representation method named as object-oriented descriptor. Using this descriptor, a set of exemplar images glocally similar to the query image is retrieved. Then, using over-segmented regions of these retrieved exemplars, a discriminative classifier is learned on-the-fly and subsequently used to predict foreground probability for the query image. Finally, the optimal segmentation is obtained by combining the online prediction with typical energy optimization of Markov random field. The proposed approach has been extensively evaluated on three datasets, including Pascal VOC 2010, VOC 2011 segmentation challenges, and iCoseg dataset. Experiments show that the proposed approach outperforms state-of-the-art methods and has the potential to segment large-scale images containing unknown objects, which never appear in the exemplar images.
Sponsorship	IEEE Signal Processing Society
Page Count	13
File Size	3301702
Starting Page	2109
Ending Page	2121
File Format	PDF
ISSN	10577149
Volume Number	23
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Support vector machines Vectors Training Prediction algorithms Kernel Optimization image retrieval Object segmentation figure-ground segmentation online glocal transfer
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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