Fuzzy theoretic model based analysis of image features

Authors Mohit Kumar
Sromona Chatterjee
Weiping Zhang
Jingzhi Yang
Lutz M. Kolbe
Title Fuzzy theoretic model based analysis of image features
Type article
Journal Information Sciences
Publisher Elsevier
Number 4
Volume 480
Month April
Year 2019
Pages 34-54
SCCH ID# 19011

Recently, membership functions based image descriptors were introduced as competing alternative to the classical histograms based image descriptors. The design of a suitable mathematical criterion for matching image descriptors to detect the correspondences be- tween the images remains as one of the basic problems of image matching and computer vision. This study derives analytically a fuzzy theoretic model of local image features to facilitate a mathematical analysis of the correspondences between descriptors of multiple images. The analytical model of the local image features defines a membership function on the descriptors as a finite mixture of the descriptor’s memberships to different descriptor- prototypes. The so-defined membership function involves parameter vectors with a special structure such that all elements of the vector are non-negatives and sum to unity. These parameter vectors are considered as uncertain and are modeled by Dirichlet type mem- bership functions. The membership functions are determined analytically by solving a de- terministic constrained optimization problem using variational optimization. The member- ship functions based analysis leads to significantly more accurate and reliable multi-image matching algorithm that can be applied under different scenarios including that of Collage creation and fully automated image clustering.