Robust and discriminative image representation: fractional-order Jacobi-Fourier moments
作者：
Highlights：
• We define a new set of generic orthogonal moments, called Fractional-order Jacobi-Fourier Moments (FJFM), which is a generic version of many existing classical and fractional-order moments.
• We develop a novel framework to improve both the robustness and discrimination power of image global representation, named Mixed Low-order Moments Feature (MLMF), by fully exploiting the time-frequency analysis property of FJFM.
• Extensive experimental results and a real-world application are given to verify the advantages of the proposed image representation scheme, with respect to robustness and discriminability.
摘要
•We define a new set of generic orthogonal moments, called Fractional-order Jacobi-Fourier Moments (FJFM), which is a generic version of many existing classical and fractional-order moments.•We develop a novel framework to improve both the robustness and discrimination power of image global representation, named Mixed Low-order Moments Feature (MLMF), by fully exploiting the time-frequency analysis property of FJFM.•Extensive experimental results and a real-world application are given to verify the advantages of the proposed image representation scheme, with respect to robustness and discriminability.
论文关键词：Image representation,Fractional,Jacobi-Fourier moments,Robustness,Discriminability
论文评审过程：Received 15 December 2019, Revised 18 January 2021, Accepted 8 February 2021, Available online 17 February 2021, Version of Record 26 February 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.107898
