NDLI: A supervised nonlinear neighborhood embedding of color histogram for image indexing

Content Provider	IEEE Xplore Digital Library
Author	Xian-Hua Han Yen-Wei Chen Sukegawa, T.
Copyright Year	2008
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Ritsumeikan Univ., Kasatsu (Yen-Wei Chen) \|\| Remixpoint Co. Ltd., Tokyo (Sukegawa, T.) \|\| Electron. & Inf. Eng. Sch., Central South Univ. of Forestry & Technol., Changsha (Xian-Hua Han)
Abstract	Subspace learning techniques are widespread in pattern recognition research. They include PCA, ICA, LPP, etc. These techniques are generally linear and unsupervised. The problem of image indexing is very complicated and the processed images are usually lie on non-linear image subspaces. In this paper, we propose a supervised nonlinear neighborhood embedding algorithm which learns an adaptive nonlinear subspace by preserving the neighborhood structure of the image color space. In the proposed algorithm, we combine the idea of nonlinear kernel mapping and preserving the neighborhood structure of the samples, so it can not only gain a perfect approximation of the nonlinear image manifold, but also enhance within-class neighborhood information. Experimental results show that the proposed method outperform other linear or unsupervised subspace learning methods.
Starting Page	949
Ending Page	952
File Size	200316
Page Count	4
File Format	PDF
ISBN	9781424417650
ISSN	15224880
DOI	10.1109/ICIP.2008.4711913
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Indexing Image retrieval Image databases Principal component analysis Spatial databases Content based retrieval Independent component analysis Linear discriminant analysis Information retrieval Locality Preserving Projection subspace Learning supervised nonlinear neighborhood embedding
Content Type	Text
Resource Type	Article

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