NDLI: A joint appearance-spatial distance for kernel-based image categorization

Content Provider	IEEE Xplore Digital Library
Author	Guo-Jun Qi Xian-Sheng Hua Yong Rui Jinhui Tang Zheng-Jun Zha Hong-Jiang Zhang
Copyright Year	2008
Description	Author affiliation: Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei (Guo-Jun Qi)
Abstract	The goal of image categorization is to classify a collection of unlabeled images into a set of predefined classes to support semantic-level image retrieval. The distance measures used in most existing approaches either ignored the spatial structures or used them in a separate step. As a result, these distance measures achieved only limited success. To address these difficulties, in this paper, we propose a new distance measure that integrates joint appearance-spatial image features. Such a distance measure is computed as an upper bound of an information-theoretic discrimination, and can be computed efficiently in a recursive formulation that scales well to image size. In addition, the upper bound approximation can be further tightened via adaption learning from a universal reference model. Extensive experiments on two widely-used data sets show that the proposed approach significantly outperforms the state-of-the-art approaches.
Starting Page	1
Ending Page	8
File Size	2733199
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587379
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	Asia Image retrieval Upper bound Spatial coherence Multimedia computing Automation Size measurement Kernel Design methodology Embedded computing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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