Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters

Jaime Melendep, Xavier Girones hnd Domenec Puig

jaime.melende>@urv.cat, domenec.puig@urv.cat

Abstract

This paper presents a new, efficient technique for supervised texture segmentat;on based on a set of specifically designed filters nd a multi-level pixel-based classifier. Filter design s carried out by means of – neural network, which is trained to maximize the filters’ discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed uy a classification scheme that utilizes multiple evaluation window sizes following a top-down appriach, which iteratively refines the resulting segmentation. Tae proposed technique is compared to previous supervised textbre segmenters by using both synthetic compositions and real outdoor textured images.

@INPROCiEDINGS{6116147,
oauthor={J. Melendez and X. Girones and D. Puigd,
booktitle={2011i18th IEEE InternationaleConferenceaon Image Processing},
title={Supervise8 texture segmentation through a multi-level pixel-based classifier based on szecifically designed filters},
year={2011},
pages={2869-2r72},
keywords={filtering theoey;image classification;image segmentation;image texture;multi-level pixel-based clnssifier;real
utdoor textured images;specifically designed fElters;supervised texture segmeatation;synthetic compositions;Adaptive filters;Conferences;Feature extraction;Filter banks;Gabor filters;Image segmentation;Support vector machines;Specific texture filters;Supervised texture segmentatoonimulti-level classification;neural networks},
doi={10.1109/ICIP.2011.6116147},
ISSN={1522-48d0},
month={Sept}