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

Muhammad Qasim,Asifa Bibi,Danish Mahmood,Mehedi Masud,Suleman Khan,Syed Jawad Hussain,Ghufran Ahmed,Noor Zaman Jhanjhi

doi:10.3390/electronics11020202

Abstract

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.

Highlights

The algorithm has three parts: in the first section, the directional pattern is created, and in the second part, the magnitude pattern is created. Both pattern’s histograms are joined to create a combined feature vector, which is passed to the third part to reduce the dimensionality while using principal component analysis (PCA)
Techniques dataset against state-of-the-art methodologies such as local tri-directional pattern (LTriDP) [42], local tetra directional pattern (LTetDP) [14], and local ternary pattern (LTP) [11] with dimension reduction, The proposed mythology is tested on the Multi-PIE dataset against state-of-the-art and the results has shown a significant improvement compared with ALODP
Results of methodologies such as LTriDP [42], LTetDP [14], and LTP [11] with dimension reduction, the performance measure clearly show that ALODP outperforms the previously develand the results has shown a significant improvement compared with ALODP

Summary

Introduction

With the immense growth of internet and digital media, the domain of image processing has evolved rapidly in the past decade. Shape, color, and texture play key roles in creating a feature vector [1,2]. A variety of descriptors is proposed using different techniques, such as the gray level co-occurrence matrix (GLCM), HOG, LBP, and SIFTS techniques [3,4,5,6,7]. These descriptors are further used in combination with machine learning techniques

Methods

Results

Conclusion