Paper Title:
An Image Retrieval Model Based on Local Fisher Discriminant Analysis
  Abstract

This paper introduces an image retrieval model based on dimensionality reduction. The proposed model is divided into two main techniques, the first one is concerned with the feature extraction from image database, and the second one is performing a dimensionality reduction. In the first technique, the color histogram and Color Texture Moment are used to extract the color and texture features, respectively. In the second technique, Local Fisher Discriminant Analysis (LFDA) which is a supervised linear dimensionality reduction algorithm is used to performing dimensionality. LFDA combines the ideas of Fisher Discriminant Analysis (FDA) and Locality Preserving Projection (LPP). LFDA can preserve both manifold of data and discriminant information. Experiments demonstrate that the proposed image retrieval scheme based on dimensionality reduction can achieve satisfactory results.

  Info
Periodical
Advanced Materials Research (Volumes 255-260)
Edited by
Jingying Zhao
Pages
2057-2061
DOI
10.4028/www.scientific.net/AMR.255-260.2057
Citation
Y. M. Wang, "An Image Retrieval Model Based on Local Fisher Discriminant Analysis", Advanced Materials Research, Vols. 255-260, pp. 2057-2061, 2011
Online since
May 2011
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Price
$32.00
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