A DYNAMIC IMAGE RETRIEVAL FRAMEWORK BASED ON FUSION-BASED FEATURE EXTRACTION USING DEEP LEARNING

Images are integral to human communication, and with the rapid growth of multimedia data, finding relevant images in large archives has become a significant challenge. Content-Based Image Retrieval (CBIR) offers a solution by retrieving visually similar images based on content rather than textual annotations. Despite its potential, CBIR systems face critical challenges, including irrelevant region detection, sensitivity to variations in brightness and size, and the absence of predefined class information in datasets. To address these issues, this study proposes a CBIR framework that integrates low- and high-level features such as texture, shape, and color for robust image representation. The framework employs wavelet-based Local Ternary Pattern (LTP) for texture extraction and incorporates a dynamic weight allocation mechanism, which adapts to statistical metrics like mean and variance to enhance retrieval accuracy. Comprehensive evaluations of the Corel-1k and Corel-10k datasets demonstrate the method's effectiveness in retrieving visually similar images with high precision. The proposed approach surpasses existing techniques, including CBIR-ANR, OMCBIR, and CNN-QCSO, in terms of precision, memory efficiency, and visual quality. The result of this work for the feature types included the dataset in the Retrieval Performance on the Corel 1K Dataset of fused features, proposing a precision of 88.1, recall of 80.5, and MAP of 88.3, and the Retrieval Performance on the Corel 10K Dataset of fused features proposed the precision of 83.7, recall of 76.2, and MAP of 83.9. This study establishes a promising direction for developing efficient CBIR systems capable of handling large-scale image datasets while improving retrieval performance.