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An in-depth comparison of four texture segmentation methods

Madasu, Vamsi K. & Yarlagadda, Prasad K.D.V. (2007) An in-depth comparison of four texture segmentation methods. In Bottema, Mark J., Maeder, Anthony J., Redding, Nick, & van den Hengel, Anton (Eds.) 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), 3-5 December 2007, Glenelg, South Australia.

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Abstract

Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. This paper presents a comparative study of four texture segmentation methods based on the following features: descriptors, heuristic function, fuzzy logic and Mask based features. Many types of textures are considered for analysis. The comparative results show that descriptor based approach is the most suitable for segmenting both natural and mosaic textures whereas heuristic function based approach is most suitable for random textures. Fuzzy features based approach is found to yield better segments for regular patterns while Mask feature based approach is the best for segmenting Natural images, but fails miserably on Mosaic textures. Fuzzy C-means classification is used for achieving texture segmentation.

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1 citations in Scopus
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ID Code: 12743
Item Type: Conference Paper
Keywords: Texture Segmentation, Descriptors, Fractals, Fuzzy c, means
DOI: 10.1109/DICTA.2007.4426820
ISBN: 0769530672
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2007 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 29 Feb 2008
Last Modified: 29 Feb 2012 23:37

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