NDLI: Efficient Image Clustering using a New Image Distance

Content Provider	IEEE Xplore Digital Library
Author	Su-Lan Zhang Qing He Zhong-Zhi Shi
Copyright Year	2007
Description	Author affiliation: Chinese Acad. of Sci., Beijing (Su-Lan Zhang; Qing He; Zhong-Zhi Shi)
Abstract	A new distance for image clustering called Generalized Geodesic Distance (GGD) and an appearance-based image clustering approach called Global Geometric Clustering for Image (GGCI) are presented. Unlike the traditional distance, GGD takes into account the spatial relationships of images. Therefore, it is robust to small perturbation of images. GGCI based on GGD uses easily measured local metric information to learn the underlying global geometry of images space, then applies the extended nearest neighbor approach to cluster images. Different from the usual nearest neighbor approach, GGCI considers the density around the nearest points within manifolds embedded in high dimensional image space, which better reflects the intrinsic geometric structure of manifold. Experimental results suggest that the proposed GGCI approach achieves lower error rates in image clustering.
Starting Page	1601
Ending Page	1605
File Size	793852
Page Count	5
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370401
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Euclidean distance Geophysics computing Robustness Nearest neighbor searches Machine learning Cybernetics Level measurement Manifolds Retina Photoreceptors Image clustering Geodesic distance Manifold
Content Type	Text
Resource Type	Article

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