Image retrieval and classication using shifted. Legendre invariant moments and Radial Basis Functions Neural Networks

被引：6
|
作者：
Hjouji, A. [1 ]
EL-Mekkaoui, J. [2 ]
Jourhmane, M. [1 ]
Qjidaa, H. [3 ]
Bouikhalene, B. [2 ]
机构：
[1] Sultan Moulay Slimane Univ, Fac Sci & Technol, Beni Mellal, Morocco
[2] Sultan Moulay Slimane Univ, Polydisciplinary Fac, Beni Mellal, Morocco
[3] Sidi Mohammed Ben Abdellah Univ, Fes, Morocco
来源：
SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING IN DATA SCIENCES (ICDS2018) | 2019年 / 148卷
关键词：
Shitted tcgcndre orthogonal polynoinials; shifted tcgcndre orthogonal invariant inoinents; Image Retrieval; Image classification; Radial Basis functions Neural Networks (RBF); ROTATION; FORMS;
D O I：
10.1016/j.procs.2019.01.019
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Since shape is one of the important low-level features of any Image Retrieval and any image classification system, we present in this paper a new set of orthogonal polynomials called shifted Legendre polynomials, this helps to build a set of orthogonal moments, which are Mvariant to translation, rotation and scale. We apply new image Retrieval and classification systems based on the proposed invariant moments and Radial Basis Functions Neural Networks. To show the effectiveness of our approaches we present some experimental results. (C) 2019 The Authors. Published by Elsevier B. V.
引用
收藏
页码：154 / 163
页数：10
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