Literature survey on Feature Extraction methods using CBIR Visual Search
Main Article Content
Abstract
Efficient image retrieval relies on robust feature extraction methods capable of capturing the distinct characteristics of color, texture, and shape. This study investigates diverse techniques across these domains, emphasizing their impact on the ranking accuracy of retrieved images. In the color domain, methods such as Color Moments (CM), Color Moment Invariant Model (CMI), Dominant Color-Based Vector Quantization (DCVQ), MPEG-7 Dominant Color Descriptor, and integrated color-texture approaches are explored for their precision in identifying chromatic variations. Texture feature extraction techniques, including Discrete Wavelet Transform (DWT), Statistical Edge Detection (SED), Modified Scalable Descriptor (MSD), and Local Derivative Radial Patterns (LDRP), alongside Support Vector Machine (SVM) classifiers, are assessed for their ability to identify and rank images based on structural complexity. For shape features, advanced techniques such as boundary moments, complex coordinates, curvature scale space, intersection point mapping, and merging strategies are evaluated for their role in preserving spatial and geometric fidelity. By examining these methods in the context of top-ranked image retrieval, this work provides a comparative framework to guide the selection of optimal feature extraction techniques for high-performance image analysis systems.