Fast and accurate computation of Racah moment invariants for image classification. (July 2019)

• Record Type: Journal Article
• Authors: Benouini, Rachid; Batioua, Imad; Zenkouar, Khalid; Zahi, Azeddine; Fadili, Hakim El; Qjidaa, Hassan
• Is Part Of: Pattern recognition. Volume 91(2019:Jul.)
• Page Start: 100
• Page End: 110
• Publication Date: 2019-07
• Subjects: Racah moment invariants -- Racah polynomials -- Fast algorithm -- Accurate computation -- Direct method -- Recursive method -- Image classification -- Pattern recognition; RM Racah Moments -- RMI Racah Moment Invariants -- GMI Geometric moment Invariants -- RST Rotation, Scaling and Translation -- TMI Tchebichef Moment Invariants -- KMI Krawtchouk Moment Invariants -- HMI Hahn Moment Invariants -- dHMI dual Hahn Moment Invariants -- ROI Region Of Interest; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.02.014
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper introduces new set of moment invariants for image classification. This paper provides a fast and accurate method for computing moment invariants. Numerical experiments are performed to demonstrate its validity and superiority. Abstract: In this paper, a new set of moment invariants, named Racah Moment Invariants (RMI), is introduced in the field of image analysis. This new set can be used to describe pattern feature independently of Rotation, Scaling and Translation transforms. Moreover, new fast and accurate algorithm, using recursive method, is developed for accelerating the computation time of the newly proposed invariants, as well as, for enhancing their numerical stability. Subsequently, several experiments have been performed. Initially, the numerical stability and computational cost are depicted. Secondly, the global and local features extraction are clearly illustrated. Then, invariability property and noise robustness are investigated. Finally, the discrimination power and the classification accuracy of the proposed invariants are extensively tested on several publicly available databases. The presented theoretical and experimental results, clearly show that the proposed method can be extremely useful in the fields of image classification.