NDLI: Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyang Tan Songcan Chen Jun Li Zhi-Hua Zhou
Copyright Year	2006
Description	Author affiliation: Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China (Xiaoyang Tan)
Abstract	The performance of many computer vision and machine learning algorithms critically depends on the quality of the similarity measure defined over the feature space. Previous works usually utilize metric distances which are ofen epistemologically different from the perceptual distance of human beings. In this paper a novel non-metric partial similarity measure is introduced, which is born to automatically capture the prominent partial similarity between two images while ignoring the confusing unimportant dissimilarity. This measure is potentially useful in face recognition since it can help identify the inherent intra-personal similarity and thus reducing the influence caused by large variations such as expression and occlusions. Moreover; to make this method practical, this paper proposes an automatic and class-dependent similarity threshold setting mechanism based on the maximal margin criterion, and uses a Self- Organization Map-based embedding technique to alleviate the computational problem. Experimental results show the feasibility and effectiveness of the proposed method.
File Size	5960478
Starting Page	168
Ending Page	145
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.170
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Extraterrestrial measurements Clustering algorithms Computer vision Humans Pattern recognition Image matching Space technology Image databases Euclidean distance Robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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