NDLI: Improving local learning for object categorization by exploring the effects of ranking

Content Provider	IEEE Xplore Digital Library
Author	Tien-Lung Chang Tyng-Luh Liu Jen-Hui Chuang
Copyright Year	2008
Description	Author affiliation: Inst. of Inf. Sci., Acad. Sinica, Taipei (Tien-Lung Chang; Tyng-Luh Liu)
Abstract	Local learning for classification is useful in dealing with various vision problems. One key factor for such approaches to be effective is to find good neighbors for the learning procedure. In this work, we describe a novel method to rank neighbors by learning a local distance function, and meanwhile to derive the local distance function by focusing on the high-ranked neighbors. The two aspects of considerations can be elegantly coupled through a well-defined objective function, motivated by a supervised ranking method called P-norm push. While the local distance functions are learned independently, they can be reshaped altogether so that their values can be directly compared. We apply the proposed method to the Caltech-101 dataset, and demonstrate the use of proper neighbors can improve the performance of classification techniques based on nearest-neighbor selection.
Starting Page	1
Ending Page	8
File Size	3176922
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587623
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	Nearest neighbor searches Information science Computer science Supervised learning Application software Computer vision Neural networks Organizing Training data Euclidean distance
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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