Unsupervised texture-based image segmentation through pattern discovery

Authors:
Jaime Melendez;Miguel Angel Garcia;Domenec Puig;Maria Petrou
Affiliations:
Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Av. Paisos Catalans 26, 43007 Tarragona, Spain;Department of Electronic and Communications Technology, Autonomous University of Madrid, Francisco Tomas y Valiente 11, 28049 Madrid, Spain;Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Av. Paisos Catalans 26, 43007 Tarragona, Spain;Communications and Signal Processing Group, Department of Electrical and Electronic Engineering, Imperial College, South Kensington Campus, London SW7 2AZ, United Kingdom
Venue:
Computer Vision and Image Understanding
Year:
2011

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Abstract

This paper presents a new efficient technique for unsupervised segmentation of textured images that aims at incorporating the advantages of supervision for discriminating texture patterns. First, a pattern discovery stage that relies on a clustering algorithm is utilized for determining the texture patterns of a given image based on the outcome of a multichannel Gabor filter bank. Then, a supervised pixel-based classifier trained with the feature vectors associated with those patterns is used to classify every image pixel into one of the sought texture classes, thus yielding the final segmentation. Multi-sized evaluation windows following a top-down approach are utilized during pixel classification 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. The proposed technique is also compared with alternative texture segmentation approaches.