Feature descriptors using super-pixels as fuzzy numbers

ABSTRACTThe objective of this paper is to extract directly local important region descriptors using image super-pixels and fuzzy numbers. Previous works are based on extracting important feature points like corners in an image then region descriptors are formed around these features. Our novel contribution is to consider directly the most discriminative super-pixels as region descriptors. First, each super-pixel is considered as a fuzzy number. Then the alpha-cut which best represents the fuzzy number is obtained. Finally, according to these alpha-cuts and the cardinality of each fuzzy number the region descriptors are formed. Matching is done according to distances between fuzzy numbers. The Palm-print recognition problem was chosen to show the effectiveness of this approach.KEYWORDS: Regions descriptorssuper-pixelsfuzzy number Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsIssam DagherIssam Dagher finished his MS in electrical engineering degree in 1994 from Florida International University, Miami, USA. He finished his Ph.D. in 1997 at the University of Central Florida, Orlando USA. He is now a full professor at the University of Balamand, Lebanon. His areas of interest are pattern recognition, neural networks, artificial intelligence, and computer vision. He published many papers on these topics.