An Acceleration Scheme to The Local Directional Pattern

Yasin Musa Ayami, Aboubayda Shabat

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

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