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CLC number: TP391

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Received: 2009-05-26

Revision Accepted: 2009-08-10

Crosschecked: 2009-11-11

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.12 P.1720~1737


Image feature optimization based on nonlinear dimensionality reduction

Author(s):  Rong ZHU, Min YAO

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   zr@zju.edu.cn, myao@zju.edu.cn

Key Words:  Image feature optimization, Nonlinear dimensionality reduction, Manifold learning, Locally linear embedding (LLE)

Rong ZHU, Min YAO. Image feature optimization based on nonlinear dimensionality reduction[J]. Journal of Zhejiang University Science A, 2009, 10(12): 1720~1737.

@article{title="Image feature optimization based on nonlinear dimensionality reduction",
author="Rong ZHU, Min YAO",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Image feature optimization based on nonlinear dimensionality reduction
%A Rong ZHU
%A Min YAO
%J Journal of Zhejiang University SCIENCE A
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%N 12
%P 1720~1737
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0920310

T1 - Image feature optimization based on nonlinear dimensionality reduction
A1 - Rong ZHU
A1 - Min YAO
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 12
SP - 1720
EP - 1737
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0920310

image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between high- and low-dimensional space via a five-tuple model. nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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