COLOR TEXTURE CLASSIFICATION USING WAVELET TRANSFORM AND NEURAL NETWORK ENSEMBLES

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].