Image Retrieval of Self-Adapt Distance Measure Based on SLLE

Xiao Fen Wang; Hai Na Zhang; Xiu Rong Qiu; Jiang Ping Song; Ke Xin Zhang

doi:10.4028/www.scientific.net/AMR.989-994.3675

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Image Retrieval of Self-Adapt Distance Measure...

Image Retrieval of Self-Adapt Distance Measure Based on SLLE

Abstract:

Self-adapt distance measure supervised locally linear embedding solves the problem that Euclidean distance measure can not apart from samples in content-based image retrieval. This method uses discriminative distance measure to construct k-NN and effectively keeps its topological structure in high dimension space, meanwhile it broadens interval of samples and strengthens the ability of classifying. Experiment results show the ADM-SLLE date-reducing-dimension method speeds up the image retrieval and acquires high accurate rate in retrieval.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

3675-3678

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.3675

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Online since:

July 2014

Authors:

Xiao Fen Wang*, Hai Na Zhang, Xiu Rong Qiu, Jiang Ping Song, Ke Xin Zhang

Keywords:

Identification Information, Manifold Learning, Self-Adapt Distance Measure

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