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Title |
WAVELET BASED FEATURES FOR COLOR TEXTURE CLASSIFICATION WITH APPLICATION TO CBIR |
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Author |
P.S.Hiremath, S. Shivashankar, Jagadeesh Pujari |
| Citation |
Vol. 6 No. 9 pp. 124-133 |
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Abstract |
This paper describes an algorithm for texture feature extraction using wavelet decomposed coefficients of an image and its complement. Four different approaches to color texture analysis are tested on the classification of images from the VisTex database. The first method employs multispectral approach, in which texture features are extracted from each channel of the RGB color space. The second method uses HSV color space in which texture features are extracted from the luminance channel V and color features from the chromaticity channels H and S. The third method uses YCbCr color space, in which texture features are extracted from the luminance channel Y and color features from the chromaticity channels Cb and Cr. The last one uses gray scale texture features computed for a color image. The classification results show that the multispectral method gives the best percentage of 97.87%. Further, this multispectral method for texture classification is applied to RBIR system. Experiments are carried out on Wang’s dataset using JSEG for segmentation. The results are encouraging. Experiments are also carried out to study the effect of segmentation on the retrieval performance. |
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Keywords |
Texture, wavelet transform, classification, feature extraction, RBIR. |
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