Self-Similarity Descriptor Extraction Algorithm Implementation

This implementation provides code for extracting self-similarity descriptors, following the algorithm described in "Matching Local Self-Similarities across Images and Videos". Self-similarity descriptors represent a method for comparing similarities between local regions in images and videos. The primary objective of this algorithm is to identify and match similar local patterns across different images and video sequences, making it applicable to various domains including image retrieval, object tracking, and video analysis. The implementation typically involves calculating correlation surfaces around keypoints and converting them into compact descriptor vectors through logarithmic-polar binning. This research significantly contributes to enhancing the effectiveness of image and video processing techniques by providing robust matching capabilities under various transformations and lighting conditions. The code structure follows the paper's methodology, incorporating essential components for patch comparison, distance computation, and descriptor normalization.