Research on similarity measurement for texture image retrieval

PLoS One. 2012;7(9):e45302. doi: 10.1371/journal.pone.0045302. Epub 2012 Sep 25.

Abstract

A complete texture image retrieval system includes two techniques: texture feature extraction and similarity measurement. Specifically, similarity measurement is a key problem for texture image retrieval study. In this paper, we present an effective similarity measurement formula. The MIT vision texture database, the Brodatz texture database, and the Outex texture database were used to verify the retrieval performance of the proposed similarity measurement method. Dual-tree complex wavelet transform and nonsubsampled contourlet transform were used to extract texture features. Experimental results show that the proposed similarity measurement method achieves better retrieval performance than some existing similarity measurement methods.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Artificial Intelligence
  • Computer Simulation
  • Databases, Factual
  • Image Enhancement
  • Image Interpretation, Computer-Assisted / methods*
  • Information Storage and Retrieval
  • Pattern Recognition, Automated / methods*
  • Pattern Recognition, Automated / statistics & numerical data
  • Wavelet Analysis

Grants and funding

This work was supported by the National Science Foundation of China under Grants 90820306 and 61072148. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.