Fuzzy Memberships Descriptors for Images
The fuzzy membership functions based local image descriptors are introduced as a competing alternative to widely accepted histograms based image descriptors. The fuzzy memberships descriptors are highly distinctive and thus facilitate an accurate image matching. This work utilizes fuzzy memberships descriptors to design a method meant for image matching. The method finds the correspondence between the two images. The study also introduces Gamma mixture fuzzy model for detecting geometrically consistent correspondence between the two images. The Gamma mixture fuzzy model combines a finite number of Gamma distributions through a fuzzy model. The parameters of Gamma mixture fuzzy model are inferred by a method similar to the variational Bayes. The image matching examples provided in the text support the claim of fuzzy memberships descriptors being highly distinctive. The method was also applied on 2D ear images for an automated human identification. The experimental results achieved the rank-one recognition accuracy of 97.5659% on a database of 125 subjects containing 493 ear images. The motivation of this study is derived from the application potential of fuzzy membership functions in characterizing the local image features.