Since the RGB color space does not closely match the human visual perception and has no ability to describe the spatial structures

the Gaussian color model

which uses the spatial and color information in an integrated model

is used to obtain more complete image features. A color-texture approach based on the Gaussian color model and a multi-scale filter bank is introduced to classify the porcelain shard images. First

the RGB color space of the image is transformed into the Gaussian color model and then the normalized multi-scale LM filter bank is used to construct the filtered images on three channels. Afterwards

the primary feature images are found by using principal components analysis and the maximum responses of the Laplacian of Gaussian filters and Gaussian filters are separately selected. These images compose a feature image set

in which the feature parameters are extracted. Finally

a support vector machine is used to learning and classification. From experimental results

the proposed method is better than gray-based method

RGB-based method and RGB_bior 4.4 wavelet based method. It can achieve a classification accuracy of 96.7% on Outex texture database and a classification accuracy of 94.2% on porcelain shard images. This method can be used in other color texture classification tasks.