Improving the Machine Learning Performance for Image Recognition Using a New Set of Mountain Fourier Moments

Abstract

The orthogonal moments are giving relevant results of these last years within the framework of object detection, pattern recognition and image reconstruction. This article is based on orthogonal functions called "Orthogonal Mountain functions (OMFs)" and we introduce a new set of moments called the multichannel Mountain Fourier moments (MMFMs), their performance is in reconstruction, noise invariants, rotation, scale and translation for image color. To validate these proposed techniques, we made several experimental tests to analyse images. We compare the results obtained from invariant moments and other current orthogonal invariant moments; the experiments show the power of the proposed moments.

Keywords

• Multichannel invariant moments
• Pattern recognition
• Orthogonal Mountain functions
• Mountain-Fourier invariant moments.
• K-nearest neighbours (KNN)
• support vector machine (SVM)