Year-end Sale % OFF 
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
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
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
