Image feature extraction techniques: A comprehensive review

Abstract

This comprehensive review explores the landscape of image feature extraction techniques, which form the cornerstone of modern image processing and computer vision applications. Feature extraction serves the critical function of transforming raw image data into informative and compact representations, enabling efficient analysis, recognition, and classification. The paper systematically categorizes and analyzes methods based on geometric, statistical, texture, color, and conceptual features. Geometric features capture structural relationships and object shapes, while statistical features provide quantitative descriptors of intensity distributions. Texture-based techniques such as Local Binary Patterns (LBP) and Gray Level Co-occurrence Matrix (GLCM) highlight surface characteristics and spatial patterns. Color features, including histograms and moments, model chromatic information vital for retrieval and segmentation tasks. The review also discusses the emerging role of deep learning in extracting hierarchical and abstract features, which offer superior adaptability and semantic richness. For each category, the strengths, limitations, computational efficiency, and domain-specific applicability are critically evaluated. The paper concludes by emphasizing the merits of multi-feature fusion approaches that integrate diverse descriptors to enhance robustness and accuracy in image understanding tasks. This survey aims to guide future research by offering a foundational and comparative perspective on classical and contemporary feature extraction strategies.