Image Content Retrieval System with Multimodal Feature Fusion

Traditional image retrieval systems face significant challenges in bridging the semantic gap between low-level visual features and high-level conceptual understanding. This paper proposes a novel multimodal image retrieval system that integrates visual, semantic, and textual features through an adaptive fusion mechanism. The framework employs attentionbased weight learning to dynamically adjust modality contributions based on query characteristics, complemented by graph neural networks for modeling inter-modal relationships. A hierarchical indexing structure using locality-sensitive hashing enables efficient large-scale retrieval with logarithmic complexity. Experimental evaluation on COCO, ImageNet, and Flickr30K datasets demonstrates superior performance, achieving 87.7% mean average precision while maintaining 52.3 ms query processing time for 100 K images. The system outperforms state-of-the-art methods by 6.3% through effective multimodal synergy, with ablation studies confirming the critical role of adaptive fusion. The proposed approach offers practical solutions for e-commerce, medical imaging, and multimedia applications requiring accurate and efficient content-based retrieval.