The need for more sophisticated and effective picture retrieval methods is rising because of the quick rise in image creation and sharing, particularly from mobile devices with high-resolution cameras. Accurate picture searching no longer requires traditional techniques that rely on metadata or simple pixel comparisons. Purpose: The goal of this work is to create a successful Content-Based Image Retrieval (CBIR) system that improves the speed and accuracy of obtaining pertinent photos from sizable databases. Design/Methodology/Approach: To transform picture content into comprehensive feature vectors, the suggested CBIR system combines several visual aspects, including color, texture, and shape, with updated image descriptors. Multi-feature analysis is then performed to extract similar images from these vectors. Findings: Compared to traditional techniques, the suggested system considerably increases retrieval accuracy and processing time, according to an evaluation on a dataset of 1,000 photos split into 11 categories. Research Limitations/Implications: The study only included 1,000 photos, and additional testing on bigger and more varied datasets could be necessary to confirm scalability and generalization. Practical Implications: The system improves user experience in picture retrieval applications like digital libraries, social media, and e-commerce by providing a scalable and reliable method for platforms and organizations managing large image databases. Originality/Value: By combining multi-feature picture analysis with optimized descriptors, this study offers a fresh approach to long-standing CBIR problems and opens the door for scalable, real-time image search in contemporary settings.
Pati et al. (Wed,) studied this question.
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