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

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 with certain neighboring pixels providing discrete information. proposed in this work utilizes the local intensity all eight directions its neighborhood. octa-directional pattern results two patterns, i.e., magnitude and directional, is quantized into 40-bin histogram. A joint histogram created by concatenating directional histograms. To measure similarities between images, Manhattan distance used. Moreover, to maintain computational cost, PCA applied, which reduces dimensionality. methodology tested on subset Multi-PIE face dataset. dataset contains almost 800,000 images over 300 people. These carries different poses have wide range facial expressions. Results were compared state-of-the-art namely, tri-directional (LTriDP), tetra (LTetDP), ternary (LTP). model supersede previously defined terms precision, accuracy, recall.