2024/07/26 by Zeinab Sedaghatjoo, Hossein Hosseinzadeh, Sedaghatjoo, Zeinab +3
Chemistry · Engineering · Mathematics · #Advanced Algorithms and Applications #Artificial intelligence #Binary number #Chemistry #Chromatography #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Extraction (chemistry) #FOS: Computer and information sciences #FOS: Mathematics #Feature (linguistics) #Feature extraction #Histogram #Image (mathematics) #Local binary patterns #Mathematics #Numerical Analysis (math.NA) #Pattern recognition (psychology)
paper · pdf · doi:10.48550/arxiv.2407.18665
openalex publication_date 2024/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The rapid growth of image data has led to the development of advanced image processing and computer vision techniques, which are crucial in various applications such as image classification, image segmentation, and pattern recognition. Texture is an important feature that has been widely used in many image processing tasks. Therefore, analyzing and understanding texture plays a pivotal role in image analysis and understanding.Local binary pattern (LBP) is a powerful operator that describes the local texture features of images. This paper provides a novel mathematical representation of the LBP by separating the operator into three matrices, two of which are always fixed and do not depend on the input data. These fixed matrices are analyzed in depth, and a new algorithm is proposed to optimize them for improved classification performance. The optimization process is based on the singular value decomposition (SVD) algorithm. As a result, the authors present optimal LBPs that effectively describe the texture of human face images. Several experiment results presented in this paper convincingly verify the efficiency and superiority of the optimized LBPs for face detection and facial expression recognition tasks.