Fuzzy Membership Descriptors for Images

The fuzzy-membership-function-based local image descriptors are introduced as a competing alternative to widely accepted histogram-based image descriptors. The fuzzy membership descriptors are highly distinctive and, thus, facilitate an accurate image matching. This study utilizes fuzzy membership descriptors to design a method meant for image matching. The method finds the correspondence between the two images. The study also introduces a
Gamma mixture fuzzy model
to detect 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 the Gamma mixture fuzzy model are inferred by a method similar to the variational Bayes. The experimental studies support the claim of fuzzy membership descriptors being highly distinctive. The method was also applied to 2-D ear images for an automated human identification. The experimental results achieved the rank-1 recognition accuracy of
$\text{97.5659}\%$
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.