PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

Qasim, Muhammad, Mahmood, Danish, Bibi, Asifa, Masud, Mehedi, Ahmed, Ghufran, Khan, Suleman, Jhanjhi, Noor Zaman and Hussain, Syed Jawad (2022) PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval. Electronics, 11 (2). p. 202. ISSN 2079-9292

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Official URL: https://doi.org/10.3390/electronics11020202


This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.

Item Type: Article
Additional Information: Funding information: This work is supported by Taif University Researchers Supporting Project number (TURSP-2020/10) Taif University, Taif, Saudi Arabia.
Uncontrolled Keywords: texture classification; local binary patterns (LBP); local ternary patterns (LTP); local tri-directional pattern (LTriDP); local tetra directional pattern (LTetDP); principal component analysis (PCA)
Subjects: G400 Computer Science
G500 Information Systems
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 11 Jan 2022 08:45
Last Modified: 11 Jan 2022 08:45
URI: http://nrl.northumbria.ac.uk/id/eprint/48124

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