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Neural Network-Based Segmentation of Textures Using Gabor Features

Ramakrishnan, AG and Raja, Kumar S and Ram, Ragu HV (2002) Neural Network-Based Segmentation of Textures Using Gabor Features. In: 2002 12th IEEE Workshop on Neural Networks for Signal Processing, 4-6 September, Newyork, pp. 365-374.

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Abstract

The effectiveness of Gabor filters for texture segmentation is well known. In this paper, we propose a texture identification scheme, based on a neural network (NN) using Gabor features. The features are derived from both the Gabor cosine and sine filters. Through experiments, we demonstrate the effectiveness of a NN based classifier using Gabor features for identifying textures in a controlled environment. The neural network used for texture identification is based on the multilayer perceptron (MLP) architecture. The classification results obtained show an improvement over those obtained by K-means clustering and maximum likelihood approaches.

Item Type: Conference Paper
Additional Information: Copyright 1990 IEEE. 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.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 19 Jan 2006
Last Modified: 19 Sep 2010 04:22
URI: http://eprints.iisc.ernet.in/id/eprint/5152

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