Soft Computing Approach for Color Image Segmentation and Texture Classification

Abstract

Color image segmentation and texture classification are challenging tasks in image analysis. This thesis presents novel techniques for color image segmentation and texture classification using soft computing methodologies. The mathematical foundation of Histon has been presented here. The histon is a contour plotted on the top of the histograms of the primary color com- ponents of a color image. It exploits the correlation among the neighboring pixels in the same plane as well as the other color planes. The concept of roughness index has been introduced to correlate the histogram and the his- ton. The roughness index plotted against the intensity, exhibits crests and troughs similar to the histogram with well defined peak and valley points. The proposed color thresholding algorithm based on the histon roughness index, yields considerable improvements in the segmentation performance when compared with other conventional techniques.