Abstract

The retrieval of images based on content has become increasingly significant in the context of digital media multimedia databases and visual information systems. This research proposes a novel approach for content-based image retrieval (CBIR) utilizing color features as the primary mechanism for indexing and querying images. The study emphasizes the role of color histograms color moments and perceptual color spaces in improving retrieval accuracy and efficiency. By integrating feature extraction techniques with similarity measurement algorithms the proposed methodology enhances the identification of relevant images in large-scale datasets. Empirical evaluation using standard benchmark image databases demonstrates improved precision recall and retrieval speed compared to conventional CBIR approaches. The research highlights the implications of color feature-based retrieval for multimedia applications digital libraries and computer vision systems offering a robust and scalable framework