Introduction to the Color Structure Code and its Implementation

One of the most important tasks of an image analysis system is image segmentation, the identification of homogeneous regions in an image. In the literature several methods for segmentation are distinguished. Common are edge detection, split and merge, region growing and clustering techniques. Most of the extensive research on image segmentation in the last three decades has been done for gray scale images. However, as the technical equipment for color acquisition becomes cheaper and more common, color image analysis becomes more and more important. Nearly all techniques for gray scale image segmentation have been transferred to color images. Most papers on color segmentation follow the clustering method. Here the pixels are mapped to feature vectors in a feature space. Now statistical methods are applied to find some clusters in this feature space. These clusters, re-mapped to the image, form the color segments. A well-known clustering technique is recursive histogram splitting, applied by many researchers. The advantage of clustering methods is the global view of the data often in form of histograms. However, although histograms provide a global view of the feature data, they do not represent the spatial information of the underlying image. The extension of clusters in feature space is often ambiguous and the statistical methods trying to solve this problem are computationally expensive. Region growing techniques start with initial cells, pixels or small regions. The pure local methods tend to chaining mismatches by merging differently colored segments. The centroidlinkage techniques are sequential methods and are therefore dependent on the choice of starting point and the order in which the pixels are processed. The CSC is a method combining the advantages of local (simplicity and fastness) and global (robustness and accuracy) techniques. It is a hierarchical region growing method that is inherently parallel and therefore independent of the choice of the starting point and the order of processing. It uses local and global information and achieves very robust segmentation results in natural color scenes. This paper describes the color segmentation system called CSC (Color Structure Code) and its implementation.