Optimal Gabor filter design for texture segmentation using stochastic optimization

Du Ming Tsai, Song Kuaw Wu, Mu-Chen Chen

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

In this paper we consider the issue of designing a single Gabor filter for multiple texture segmentation using a systematic optimization algorithm. The proposed algorithm is a stochastic search technique based on the simulated annealing (SA) procedure. It embeds the pattern search into the SA procedure as the move generation mechanism to accelerate the search. The selection objective for a best Gabor filter is based on the Maxmin principle that maximizes the minimum energy ratio of any two distinct textures in question. The objective makes the energy responses between different texture classes well separated. Therefore, a simple thresholding scheme can be directly applied to partition an input image into differently textured regions. The experiments on a number of bipartite, tripartite and quadripartite textured images have shown promising results using the proposed method.

Original languageEnglish
Pages (from-to)299-316
Number of pages18
JournalImage and Vision Computing
Volume19
Issue number5
DOIs
StatePublished - 1 Jan 2001

Fingerprint Dive into the research topics of 'Optimal Gabor filter design for texture segmentation using stochastic optimization'. Together they form a unique fingerprint.

Cite this