Neuro-fuzzy clustering approach for quadtree Segmentation of images

Segmentation of images is used for several purposes such as estimation of the boundary of an object, shape analysis, contour detection, texture segmentation and classification of objects within an image. Despite the existence of several methods and techniques for segmenting images, this task still remains a crucial problem. In our research we have developed a neural-network based fuzzy clustering technique to segment images into regions of specific interest using a quadtree segmentation approach. Since different regions of an image contain varying amount of detail, it is advantageous to segment the regions into blocks of different sizes depending on the content of information present within each block. As the global features of an image are distributed over a wider span of the image and the finer details are concentrated in limited regions, a quadtree segmentation algorithm can efficiently tackle the problem of segmenting images of all kinds. However, block based techniques tend to introduce blocking artifacts and this problem can be avoided by using a neuro-fuzzy clustering scheme to merge the neighboring blocks of similar regions in a smooth fashion. The proposed algorithm has been applied to images of different kinds and has yielded promising results.