Image feature extraction techniques: A comprehensive review

Abstract

• Comprehensive Evaluation : Presents an in-depth review of image feature extraction techniques, covering geometrical, statistical, texture, and color-based features. • Geometrical Features : Explores techniques capturing shapes and spatial relationships within images, essential for structural analysis. • Statistical Features : Provides insights into methods analyzing pixel intensity distributions, offering foundational understanding of image properties. • Texture Features : Highlights techniques like Local Binary Patterns (LBP) and Gray Level Cooccurrence Matrix (GLCM), emphasizing the role of spatial arrangement and repetitive patterns in surface characterization. • Color Features : Examines methods such as histograms and moments to analyze color distribution and organization for image retrieval and classification. • Critical Analysis : Evaluates the strengths, limitations, and practical applications of each feature extraction method, emphasizing their suitability for various image analysis tasks. • Integrated Approaches : Discusses the advantages of combining multiple feature types for robust and accurate image analysis, offering a pathway for future research and advancements. • Performance Insights : Concludes with a comparative analysis of merits, demerits, applications, and estimated performance metrics for various techniques. These highlights summarize the core contributions and findings of the paper, showcasing its value to researchers and practitioners in image processing and computer vision 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.

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