New Enhanced LBP Feature Extraction Method with Multi-Block Comparison

In computer vision applications, Local Binary Pattern (LBP) feature extraction has been widely adopted for image classification and recognition tasks. However, traditional LBP methodologies primarily focus on local binary patterns around individual pixels without leveraging information from surrounding pixel regions. To enhance LBP feature performance, we propose an improved feature extraction approach utilizing multi-block comparison techniques. This method implements a block-wise comparison mechanism where multiple pixel blocks are analyzed simultaneously, generating more discriminative LBP features through weighted neighborhood comparisons. The implementation typically involves dividing the image into sub-blocks, computing LBP histograms for each block, and then combining these histograms using spatial pyramid matching or feature concatenation algorithms. This enhancement significantly improves classification accuracy in various computer vision applications including facial recognition systems, object tracking implementations, pedestrian detection frameworks, and other pattern recognition tasks. The code implementation can utilize OpenCV's LBP functions combined with custom block processing routines to achieve optimal feature representation.