NDLI: Efficient scale space auto-context for image segmentation and labeling

Content Provider	IEEE Xplore Digital Library
Author	Jiayan Jiang Zhuowen Tu
Copyright Year	2009
Description	Author affiliation: Dept. of Neurology, UCLA, Los Angeles, CA, USA (Jiayan Jiang; Zhuowen Tu)
Abstract	The conditional random fields (CRF) model, using patch-based classification bound with context information, has been widely adopted for image segmentation/ labeling. In this paper, we propose three components for improving the speed and accuracy, and illustrate them on a developed auto-context algorithm: (1) a new coding scheme for multiclass classification, named data-assisted output code (DAOC); (2) a scale-space approach to make it less sensitive to geometric scale change; and (3) a region-based voting scheme to make it faster and more accurate at object boundaries. The proposed multiclass classifier, DAOC, is general and particularly appealing when the number of class becomes large since it needs a minimal number of $[log_{2}$ k] binary classifiers for k classes. We show advantages of the DAOC classifier over the existing algorithms on several Irvine repository datasets, as well as vision applications. Combining DAOC, the scale-space approach, and the region-based voting scheme for autocontext, the overall algorithm is significantly faster (5 ~ 10 times) than the original auto-context, with improved accuracy over many of the existing algorithms on theMSRC and VOC 2007 datasets.
Starting Page	1810
Ending Page	1817
File Size	938469
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206761
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Labeling Testing Voting Context modeling Pixel Neuroimaging Nervous system Computer science Large-scale systems
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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