Abstract
As digital image collections grow in size, there is an increasing need for robust image retrieval systems capable of managing large datasets effectively. This work offerings a novel Content-Based Image Retrieval (CBIR) system designed to enhance retrieval accuracy across both general and medical image datasets. The proposed system leverages U-Net for feature extraction, integrated with an improved weight-learning approach to enhance retrieval performance. A Convolutional neural network, U-Net, a network design famous for its picture segmentation ability, is utilized to capture complex, high-level image features. The approach includes an adaptive, query-aware feature weighting mechanism that applies weight re-scaling to parameterized features, assigning optimized weights to top-ranked images. This CBIR system comprises three main components: image pre-processing, U-Net-based feature extraction, and feature re-weighting. During pre-processing, images undergo augmentation and normalization to increase model robustness. The feature re-weighting process evaluates feature importance using cosine similarity, which further improves discriminative power at retrieval. The proposed CBIR system was tested across various image datasets through extensive experiments, with performance measured in terms of recall, precision and F1-score. The results indicate that integrating feature re-weighting with U-Net-based extraction significantly enhances retrieval effectiveness. This work represents a step forward in developing adaptive image retrieval systems that better respond to diverse retrieval scenarios.
Published Version
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