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Indexed by:期刊论文

Date of Publication:2015-03-15

Journal:Journal of Computational Information Systems

Included Journals:EI、Scopus

Volume:11

Issue:6

Page Number:2269-2277

ISSN No.:15539105

Abstract:With the development of information age, image information has been widely applied in various fields, and content-based image retrieval (CBIR) has become a hot topic to research. However, after extracting the underlying visual image feature, the dimensions of the image feature will be high, which will lead to a higher computation complexity and space complexity in the process of image retrieval. In order to solve this problem, a novel manifold learning algorithm, named globally and locally consistent principal component analysis (GLPCA) algorithm, is proposed. Compared with the traditional dimensionality reduction algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA), GLPCA considers the global and local characteristics of the data structure comprehensively, and it has better performance on both linear and nonlinear data. The experiment results on Corel image data set show the excellent performance of GLPCA algorithm in image retrieval. Copyright ? 2015 Binary Information Press.