Entropy guided multi level feature fusion network for high precision content based image retrieval

Content-based image retrieval (CBIR) is essential for managing and searching massive image repositories across a wide variety of applications. Nevertheless, some traditional CBIR systems exhibit low retrieval accuracy because they use predetermined feature weights, lack semantic gaps, and poorly exploit heterogeneous visual features. To overcome such difficulties, the present study will introduce a multi-feature adaptive CBIR framework that combines deep and handcrafted features using an information entropy-based fusion and a trust-based weighting system. Deep convolutional models, combined with complementary low-level descriptors, are used to extract discriminative features in the proposed approach. A PageRank-based similarity propagation strategy is also used to narrow image ranking by leveraging similarity relationships across the globe. Evaluation is performed using standard retrieval measures, such as Mean Average Precision (mAP), Precision at K, Recall at K, and NDCG. The experimental results show that the proposed approach consistently improves performance across benchmark datasets. The framework boosts mAP by up to 8.6% over traditional fixed-weight fusion methods, while Precision@10 and NDCG@10 increase by 6.2% and 7.4%, respectively. The statistical analysis shows that these improvements are significant at the 95% confidence level, indicating that retrieval behavior is robust and reliable. These findings confirm the efficiency of entropy-driven adaptive fusion and ranking refinement in overcoming the major drawbacks of current CBIR systems, and the suggested framework is appropriate for large-scale image search in practice.