Article

Color texture classification using wavelet transform and neural network ensembles

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (Impact Factor: 0.37). 01/2009; 34(2).

ABSTRACT

The wavelet domain features have been intensively used for texture classification and texture segmentation with encouraging results. More of the proposed multi-resolution texture analysis methods are quite successful, but all the applications of the texture analysis so far are limited to gray scale images. This paper investigates the usage of wavelet transform and neural network ensembles for color texture classification problem. The proposed scheme is composed of a wavelet domain feature extractor and ensembles of neural networks classifier. Entropy and energy features are integrated to the wavelet domain feature extractor. Various experiments have been carried out with different wavelet filters. The performed experimental studies show the efficacy of the proposed structure for color texture classification. The highest success rate is over 98%. Moreover, we compare our results with wavelet energy correlation signatures [2].

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Available from: Abdulkadir Sengur, Nov 13, 2014
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    • "Image texture provides the measure of properties such as coarseness, smoothness and regularity, etc. For the past decades, a number of texture analysis methods have been proposed, but most of them use grey-scale images, which represent the amount of visible light at the pixel's position, while ignoring the colour information [1] [2]. The important property all have in common is that they constitute an appropriate model for various angular relationships and distances between neighbouring cell pairs on the image and are adequately specified by a set of grey-tone spatial dependence matrices or grey-level co-occurrence matrices (GLCM). "
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    • "Finally, to increase the accuracy of classification, proposed approach is expanded to color images to utilize the ability of approach in analyzing each RGB channels, individually. The usages of wavelet transform and a neural network ensemble for color texture classification problem is investigated in (Sengur, 2009). The proposed scheme is composed of a wavelet domain feature extractor and ensembles of neural networks classifier. "
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    ABSTRACT: In this study, an efficient feature fusion based technique for the classification of colour texture images in VisTex album is presented. Gray Level Co-occurrence Matrix (GLCM) and its associated texture features contrast, correlation, energy and homogeneity are used in the proposed approach. The proposed GLCM texture features are obtained from the original colour texture as well as the first non singleton dimension of the same image. These features are fused at feature level to classify the colour texture image using nearest neighbor classifier. The results demonstrate that the proposed fusion of difference image GLCM features is much more efficient than the original GLCM features.
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    • "The usages of wavelet transform and a neural network ensemble for color texture classification problem is investigated in [5]. The proposed scheme is composed of a wavelet domain feature extractor and ensembles of neural networks classifier. "
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    ABSTRACT: In this paper, a novel classification system for colour texture images based on Gray Level Cooccurrence Matrix (GLCM) is presented. The GLCM features contrast, correlation, energy and homogeneity are used to classify the colour textures. First, the colour channels Red, Green and Blue are separated from the input colour texture image. Then the GLCM features are extracted for each colour channel of the original texture image and the difference image calculated along the first non singleton dimension of the input colour channel image. The performance of the proposed system evaluated for the colour channel individually. The robust K Nearest Neighbor (K-NN) classifier is used for the classification purpose. Experimental results show that the proposed method produces 98.30% (Red), 98.92% (Green) and 99.37% (Blue) average classification rate for 20 colour textures in the VisTex album.
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