Wavelets and support vector machines for texture classification

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Abstract

We present a novel texture classification algorithm using 2-D discrete wavelet transform (DWT) and support vector machines (SVM). The DWT is used to generate feature images from individual wavelet subbands, and a local energy function is computed corresponding to each pixel of the feature images. This feature vector is first used for training and later on for testing the SVM classifier. The experimental setup consists of images from the Brodatz and MIT VisTeX texture databases and a combination of some images therein. The proposed method produces promising classification results for both single and multiple class texture analysis problems.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title: Proceedings of INMIC 2004. 8th International Multitopic Conference, 2004.
Publisher: IEEE
ISBN: 0780386809
Official Date: 2004
Dates:
Date
Event
2004
Published
Page Range: pp. 328-333
DOI: 10.1109/INMIC.2004.1492898
Status: Peer Reviewed
Publication Status: Published
Date of first compliant deposit: 27 December 2015
Conference Paper Type: Paper
Title of Event: 8th IEEE International Multitopic Conference (INMIC 2004)
Type of Event: Conference
Location of Event: Lahore, Pakistan
Date(s) of Event: 24-26 Dec 2004
URI: https://wrap.warwick.ac.uk/61373/

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