Texture-based algorithm for color image classification

In this paper, a texture based algorithm is developed for classifying color images. The images are filtered by a set of Gabor filters at different scales and orientations. The energy of the filtered images in each channel and between channels are computed and used for classification. The normalized RGB, xyY and HIQ color spaces are used to identify the best space for classifying the color images. The best representation of the textures are found to be using normalized RGB and HIQ space and chrominance values. A filter selection process using texture similarity is adopted. Unichannel and interchannel features are computed. A feature reduction process is applied before using a classifier. The algorithm is used to classify sets of textures from databases of color texture images and it gives good results. It is also applied to Landsat TM images. The 7 channels are used and the best channels for classification of the image are found to be R and G. The algorithm has been designed to use the appropriate Gabor filters based on texture transition characteristics within and between channels. The algorithm performs better than using only the gray scale values of the color images.