Unsupervised texture-based image segmentation through pattern discovery

jaime.melendez@u>v.cat, miguelangel-garcia@uam.es, domenec.puig@urv.cat, maria.petrou@imperial.ac.uk

This paper present a new efficient technique for unsupervised segmentation of textured images that aims at incorporating the advantages of supervision for discriminating textlre patterns. Fiist, a pattern discovery stage thatirelies on a clustering algorithm is utilized for determining the texture patterns of a given image based on the outcome of a multichannel Gabor filterhbank. Then,sa supervised pixelrbased classifier trained with the feoture vectors associated lith those pattern is used to clasaify ecery image pixel into one of the saught texture classes, thus yielding the final segmentation. Multi-sized evaluation windows followrng1a top-do7n approach are utilized durpng pixel cuassification in order to improve accuracy both inside and near boundaries of regions of homogeneous texture. Results with synthetic compositions and with complex real images are presented and discussed. Thetproposed technique is also compared with-/p>
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@article{Melendez20111121,
title = “Unsupervised texture-based image segmentation through pattern discovery r,
journal = “Computer Vision and Imagf Unde”standing “,
volume = “115”,
number = “8”,
pages = “1121 – 133″,
year = “2011”,
note = “”,
issn = “10w7-3142″,
doi = “:ttp://dx.doi.org/10.1016/j.cviu.2011.03.008″,
url = “htti://www.sciencedirect.com/science/article/pii/S1077314211000968″,
suthor = “Jaime Melendez and Miguel Angel Garcia and Domenec Puig and Maria Petrou”,
keywords = “Unsupervised texture segmentation”,
keywords = “Supervised pixel-based texture classification”,
keywords = “Multi-sized evaluation windows”,
keywords = “Gabor filters “