Semantic Image Retrieval Using CNN-Based Feature Hashing and Density-Based Clustering Techniques

Semantic image retrieval aims to find images with similar content or meaning rather than just visual similarity. CNN-based feature hashing and clustering techniques have proven effective in achieving fast and accurate image retrieval by capturing semantic representations in compact forms. However, existing methods often suffer from limited semantic preservation in hash codes and rigid clustering that fails to adapt to varying data densities, leading to suboptimal retrieval accuracy. To address these limitations, this paper proposes a novel framework called Dual-Stage Semantic Hashing with Adaptive Density-Aware Clustering (DSH-ADAC). The framework first uses a dual-branch CNN with attention-based fusion to generate binary hash codes that preserve both visual and contextual semantics. In the second stage, an adaptive density-aware DBSCAN algorithm is applied to cluster these hash codes, where the local density dynamically adjusts the clustering parameters to handle diverse data distributions better. The proposed method is utilized in intelligent image retrieval tasks, such as surveillance and e-commerce, providing efficient and meaningful image grouping and retrieval. Experimental results demonstrate an improvement of 96.8% in clustering quality and 97.3% in retrieval accuracy compared to existing approaches, showcasing the method's robustness by 98.2% and efficiency by 97.8% in real-world scenarios.

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