Similarity measurement with combination of mesh distance fourier transform and global features in 2D binary image

Similarity measurements in images have always been a challenging task in the field of pattern recognition techniques. Shape based features is a widely adopted method in Content Based Image Retrieval (CBIR) for similarity measurement. In this paper we proposed an enhanced version of Mesh Distance Fourier Descriptor (MDFD) previously developed in our lab for the similarity measurement. Two extra levels of filters have been added to the output of MDFD so that the final output is more refined and the most similar image to the query image is selected from the database. The first level filter includes processing of images retrieved based on ratio of area of the image to the area of minimum bounding rectangle enclosing that image. The second level filtering includes calculation of average of absolute difference of global features like eccentricity, convexity and solidity of the query image and retrieved image. Adding these two extra filtering levels, the matching ratio has been increased from 84% to 88% which shows adding filters enhances the results of MDFD. In this paper we have used binary images extracted from region of interest (ROI) of mammogram which are classified into single objects using known classification methods such as K-means and SVM algorithms.