NDLI: Auto-context and its application to high-level vision tasks

Content Provider	IEEE Xplore Digital Library
Author	Zhuowen Tu
Copyright Year	2008
Description	Author affiliation: Lab. of Neuro Imaging, Univ. of California, Los Angeles, CA (Zhuowen Tu)
Abstract	The notion of using context information for solving high-level vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with the image appearance, remains mostly unknown. The current literature using Markov random fields (MRFs) and conditional random fields (CRFs) often involves specific algorithm design, in which the modeling and computing stages are studied in isolation. In this paper, we propose an auto-context algorithm. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates to approach the ground truth. Auto-context learns an integrated low-level and context model, and is very general and easy to implement. Under nearly the identical parameter setting in the training, we apply the algorithm on three challenging vision applications: object segmentation, human body configuration, and scene region labeling. It typically takes about 30 ~ 70 seconds to run the algorithm in testing. Moreover, the scope of the proposed algorithm goes beyond high-level vision. It has the potential to be used for a wide variety of problems of multi-variate labeling.
Starting Page	1
Ending Page	8
File Size	689022
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587436
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Layout Context modeling Markov random fields Hidden Markov models Biological system modeling Labeling Statistics Neuroimaging Algorithm design and analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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