Image Feature Extraction and Analysis

This project encompasses multiple feature extraction methodologies, with color features, texture features, shape features, and SIFT features being the core components. Color feature extraction typically involves histogram analysis or color moment calculations to differentiate elements within images. Texture features utilize algorithms like Gray-Level Co-occurrence Matrix (GLCM) or Gabor filtering to identify material patterns and surface characteristics. Shape feature extraction employs contour analysis or Hu moments to characterize geometric properties of objects. SIFT (Scale-Invariant Feature Transform) feature detection identifies keypoints and local descriptors that remain invariant to scale and rotation changes. To demonstrate practical applications, we have developed a compact Content-Based Image Retrieval (CBIR) system implementation. The codebase includes feature vector computation, similarity measurement using distance metrics like Euclidean or Cosine similarity, and retrieval ranking algorithms. This enables users to efficiently search and locate target images based on visual content similarities. Additionally, comprehensive presentation materials provide in-depth technical explanations, algorithm comparisons, and implementation guidelines to facilitate better understanding and utilization of the project's capabilities. The documentation covers feature extraction pipelines, parameter optimization strategies, and performance evaluation metrics for each implemented method.