NDLI: OBJ CUT

Content Provider	IEEE Xplore Digital Library
Author	Kumar, M.P. Ton, P.H.S. Zisserman, A.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput., Oxford Brookes Univ., UK (Kumar, M.P.; Ton, P.H.S.)
Abstract	In this paper, we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (PS) and Markov random fields (MRFs) both of which have efficient algorithms for their solution. The resulting combination, which we call the object category specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.
Sponsorship	IEEE Comput. Soc
Starting Page	18
Ending Page	25
File Size	751129
Page Count	8
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.249
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Shape Object recognition Optimization methods Bayesian methods Markov random fields Horses Object detection Image sampling Cows
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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