Color Texture Analysis Color Texture Color and texture are of considerable importance for image analysis. Color as well as texture have been discussed in literature intensively. But most of the known texture models are based on grey-level images. This is an inadequate restriction in many real world applications of computer vision. We are working on color texture models for micro-textures in natural scenes. Texture examples Color Covariance Texture Model The Color Covariance (CC) texture model is derived from the auto-covariance model for grey-level images. This texture model is defined for color images with three color planes, e.g. RGB-images. Interactive relations between different color planes are calculated by a second order statistic. Formula Color Texture Synthesis The synthesis of color texture is examined in order to find an optimal set of color texture parameters describing natural color textures. If a good re-synthesis of color textures is possible then an adequate parameter set is found. The texture synthesis in the CC model follows an evolutionary strategy. First a random color texture is generated with the same color histogram as the original texture. In several iterations all pixels of the image are exchanged in their position to make the synthesized texture more similar to the original. The significance of the color texture parameters have been examined in order to find a minimal set of color texture parameters. The number of the CC parameters used for synthesis and the quantity of the color histograms used for initialization have been reduced in many experiments. Examples of color texture synthesis Color Texture Classification Also classification is used in order to find a reduced parameter set of the CC texture model. Therefore a large set of images was built showing the bark of 408 different trees of six species. The color texture of these images are classified by a K-Nearest-Neighbour Classifier. In various classification experiments a new set of color texture parameters was proofed to work well. These parameters are related to the human perception of texture. For further reduction of the dimension of the feature space the Principal Component Analysis from statistics is applied among other methods. Editor: Raimund Lakmann Letzte Änderung: 21.11.97