An Acceleration Scheme to The Local Directional Pattern

TL;DR

This paper introduces an acceleration scheme for the Local Directional Pattern (LDP) to significantly reduce its computational time, making it more practical for real-time image analysis applications.

Contribution

A novel acceleration scheme for LDP that reduces its feature extraction time by nearly threefold, addressing its computational inefficiency.

Findings

LDP's running time is two orders of magnitude higher than GLCM.

The proposed scheme reduces LDP's running time by almost 3 times.

The scheme maintains comparable feature extraction quality.

Abstract

This study seeks to improve the running time of the Local Directional Pattern (LDP) during feature extraction using a newly proposed acceleration scheme to LDP. LDP is considered to be computationally expensive. To confirm this, the running time of the LDP to gray level co-occurrence matrix (GLCM) were it was established that the running time for LDP was two orders of magnitude higher than that of the GLCM. In this study, the performance of the newly proposed acceleration scheme was evaluated against LDP and Local Binary patter (LBP) using images from the publicly available extended Cohn-Kanade (CK+) dataset. Based on our findings, the proposed acceleration scheme significantly improves the running time of the LDP by almost 3 times during feature extraction