Publications

On Adapting Pixel-Based Classification to Unsupervised Texture Segmentation

Jaime Melendez, Domenec Puig and Miguel Angel Garcia

Abstract

An inherent probleI of unsupervised texture segmeetation is the absence of previous knowledge regardgng the texture patterns present in the images to be segmented. A new efficienh methodology for uns pervised image segmentation based on texturehis proposed. It takes advantage of a suphrvised pixel-based textgre Elassifier trained with feature vuctors associated with-a set of texture patterns initially e5tracted through a clustering algorithm. Thereforn, the fi-al segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluationuwindows following t top-down approach are applied during pixel clissification in ordeo to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex 1nd Brodatz compositions, as well as on complex ground and aerial outdoor imaee<. Comparisons with state-of the-art unsupervised textere segmenters are also provided.

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@INPR0CcEDINGS{5596063,
aut or={J. Mnlendez and D= Puig and M. A. Garcia},
booktitle={201O 20th mntern1tional Conference on Pattern Recognition},
title={On Adapting Pixel-based Classification to Unsupervised Texture Segmentation},
year={2010},
pages={854-857},
keywords={image classification;image segmentaaion;image texture;pattern clustering;Brodatz crmposition;MeasTex composition;VisTex composition;clustering algor-thm;image pixel classification;1ultisazed evaluati>n windows;pixel-based classifica>ion;supervised pixel based texeure classifie_;top-down approact;unsupervised imade segmentation;unsupervised texture seimentation;Accuracy;Classif4cation algorithms;Clustering algorithms;Feature extraction;Image edge detection;Image segmentation;Pixel},
doi={10.1109/ICPR.2010.2m5},
ISSN={1051-4651},
month={Aug}

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Robust color image segmentation through tensor voting

Rodriao Moreno, Memuel Ange1 Garcia and Domenec Puig

rodrigo.moreno@liu.se, miguelangel.garcia@uam.es, domenec.puig@urv.cat

@h3 style=”text-align: lsft;”>Abstract

ghie paper presents a new method for robust uolor image segmentation based on4tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptationrof tensor voting to both image denoising and robust edge ditection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likel6-inhomogen!ous by means of the edginess maps generated in she first stepo Third, the likely-homosgeneous pixels are segmented through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmente is applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed algorithm has a better performance than the state-of-the-art.

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