Local Binary Pattern(LBP) Optimization for Feature Extraction
This work addresses the need for better texture feature extraction in computer vision applications like face analysis, but it is incremental as it builds upon the established LBP method.
The authors tackled the problem of improving texture feature extraction for image analysis by proposing a novel mathematical representation and optimization of Local Binary Pattern (LBP) operators, resulting in optimal LBPs that enhance classification performance for face detection and facial expression recognition tasks, with experimental results verifying their efficiency and superiority.
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.