Document Detail

CTex--an adaptive unsupervised segmentation algorithm based on color-texture coherence.
MedLine Citation:
PMID:  18784039     Owner:  NLM     Status:  MEDLINE    
This paper presents the development of an unsupervised image segmentation framework (referred to as CTex) that is based on the adaptive inclusion of color and texture in the process of data partition. An important contribution of this work consists of a new formulation for the extraction of color features that evaluates the input image in a multispace color representation. To achieve this, we have used the opponent characteristics of the RGB and YIQ color spaces where the key component was the inclusion of the Self Organizing Map (SOM) network in the computation of the dominant colors and estimation of the optimal number of clusters in the image. The texture features are computed using a multichannel texture decomposition scheme based on Gabor filtering. The major contribution of this work resides in the adaptive integration of the color and texture features in a compound mathematical descriptor with the aim of identifying the homogenous regions in the image. This integration is performed by a novel adaptive clustering algorithm that enforces the spatial continuity during the data assignment process. A comprehensive qualitative and quantitative performance evaluation has been carried out and the experimental results indicate that the proposed technique is accurate in capturing the color and texture characteristics when applied to complex natural images.
Dana E Ilea; Paul F Whelan
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society     Volume:  17     ISSN:  1057-7149     ISO Abbreviation:  IEEE Trans Image Process     Publication Date:  2008 Oct 
Date Detail:
Created Date:  2008-09-11     Completed Date:  2008-10-30     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1926-39     Citation Subset:  IM    
Vision Systems Group, School of Electronic Engineering, Dublin City University, Dublin, Ireland.
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MeSH Terms
Artificial Intelligence*
Cluster Analysis
Colorimetry / methods*
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Tomography, Optical Coherence / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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