A Hybrid Deep Hashing and Metric Space Partitioning Framework for Scalable Content-Based Image Retrieval via Unsupervised Representation Learning and VP-Tree Optimization

Abstract

Background and Objectives: Content-Based Image Retrieval (CBIR) systems are crucial for managing the exponential growth of digital imagery. Traditional methods relying on handcrafted features often fail to scale and capture semantic content. Although deep learning enhances retrieval quality, challenges persist in computational complexity and efficiency. This paper introduces a hybrid CBIR framework that combines unsupervised deep feature learning, adaptive hashing, and VP-Tree-based hierarchical search optimization. The proposed system, evaluated on CIFAR-10, ImageNet subset, and a custom medical imaging dataset, achieves a mean average precision (mAP) of 96.1% and reduces retrieval latency by approximately 40% compared to conventional methods. By leveraging autoencoder-driven latent feature extraction and scalable metric space partitioning, our framework demonstrates superior performance in scalability, retrieval speed, and accuracy for largescale applications. Methods: The proposed framework employs autoencoder-driven latent space encoding to extract compact yet semantically rich feature representations, ensuring robust discriminability across diverse image categories. To enhance retrieval efficiency, a hybrid search mechanism is implemented: a Euclidean-based nearest neighbor scheme O (N log N) is used for moderate-scale datasets, while a VP-Tree-based hashing scheme O (log N) is applied for large-scale retrieval scenarios. By leveraging hierarchical metric space partitioning, the method significantly reduces search complexity while maintaining retrieval accuracy. Results: Extensive evaluations show the proposed framework outperforms traditional and modern deep hashing techniques, achieving higher mean average precision, lower search latency, and better storage efficiency for both moderate and large-scale datasets. By integrating unsupervised representation learning, advanced hashing, and optimized search structures, the system surpasses conventional methods in speed and precision. Conclusion: This study presents a highly scalable and computationally efficient CBIR framework that addresses the limitations of existing methods by combining unsupervised deep feature learning, adaptive hashing, and hierarchical search structures. The results highlight the framework's ability to achieve high retrieval accuracy and efficiency, thus making it suitable for real-time applications in large-scale multimedia repositories.