Statistical based image comparision by quad tree segmentation

The feature extraction is one of the most important steps in face analysis applications. Quadtree decomposition is an analysis technique that involves subdividing an image into blocks that are more homogeneous than the image itself. Texture features are extracted from the spatial black in every images and it performs to segmentation directly using spatial frequency data. Quad tree decomposition is the evaluation criterion of image segmentation. It works by dividing a square image into four equal-sized square blocks, and then testing each block to see if it meets some criterion of homogeneity. In this paper use quadtree decomposition for segmenting images by texture content with application to indexing images in a large image data base, measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal, measures the local variations in the gray-level co-occurrence matrix and provides the sum of squared elements in the GLCM. Compare the images are using correlation and using F test value.