NDLI: A discriminative learning framework with pairwise constraints for video object classification

Content Provider	IEEE Xplore Digital Library
Author	Rong Yan Jian Zhang Jie Yang Hauptmann, A.G.
Copyright Year	1979
Abstract	To deal with the problem of insufficient labeled data in video object classification, one solution is to utilize additional pairwise constraints that indicate the relationship between two examples, i.e., whether these examples belong to the same class or not. In this paper, we propose a discriminative learning approach which can incorporate pairwise constraints into a conventional margin-based learning framework. Different from previous work that usually attempts to learn better distance metrics or estimate the underlying data distribution, the proposed approach can directly model the decision boundary and, thus, require fewer model assumptions. Moreover, the proposed approach can handle both labeled data and pairwise constraints in a unified framework. In this work, we investigate two families of pairwise loss functions, namely, convex and nonconvex pairwise loss functions, and then derive three pairwise learning algorithms by plugging in the hinge loss and the logistic loss functions. The proposed learning algorithms were evaluated using a people identification task on two surveillance video data sets. The experiments demonstrated that the proposed pairwise learning algorithms considerably outperform the baseline classifiers using only labeled data and two other pairwise learning algorithms with the same amount of pairwise constraints.
Sponsorship	IEEE Computer Society
Page Count	16
File Size	3793217
Starting Page	578
Ending Page	593
File Format	PDF
ISSN	01628828
Volume Number	28
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Surveillance Training data Video sequences Feedback Fasteners Logistics Streaming media Video sharing Cameras margin-based learning. Video object classification pairwise constraints discriminative learning
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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