An LBP-HOG Descriptor Based on Matrix Projection For Mammogram Classification

TL;DR

This paper introduces matrix projection-based local feature descriptors, M-LBP and M-HOG, and their integration for efficient mammogram classification, achieving promising accuracy and reduced computational costs.

Contribution

It proposes novel matrix projection-based descriptors for mammogram analysis, eliminating iterative scanning and enhancing efficiency compared to traditional methods.

Findings

High classification accuracy on mammogram dataset

Reduced computational complexity

Effective combined descriptor performance

Abstract

In image based feature descriptor design, local information from image patches are extracted using iterative scanning operations which cause high computational costs. In order to avoid such scanning operations, we present matrix multiplication based local feature descriptors, namely a Matrix projection based Local Binary Pattern (M-LBP) descriptor and a Matrix projection based Histogram of Oriented Gradients (M-HOG) descriptor. Additionally, an integrated formulation of M-LBP and M-HOG (M-LBP-HOG) is also proposed to perform the two descriptors together in a single step. The proposed descriptors are evaluated using a publicly available mammogram database. The results show promising performances in terms of classification accuracy and computational efficiency.