" Optimizing Image Retrieval: Synergizing Feature Selection and Continuous Learning in a Distinctive Hybrid Framework "
Keywords:
CBIR, EHO-PSO, GA, Precision, RecallAbstract
Efficient image retrieval, encompassing color, texture, shape, and various visual attributes, stands as a critical pursuit. However, it grapples with the intricacies of diverse datasets and inherent complexities. To tackle these challenges effectively, we propose an innovative hybrid model for image retrieval, which leverages feature selection techniques, seamlessly integrating continued learning. Our strategy begins by combining Elephant Herding Optimisation (EHO) and Particle Swarm Optimisation (PSO) applies in a hybrid layer. This stratum is essential for the extraction of comprehensive data sets from multimodal images, persistently maximizing inter-class feature variance. This deliberate feature selection strategy significantly bolsters retrieval performsance. Subsequently, we introduce a Genetic Algorithm (GA)-based ranking model, tailored to process the selected feature sets. This ranking model adeptly combines multiple distance metrics, orchestrating a harmonious balance between computational efficiency and retrieval precision. Further enhancing image rankings, we employ an incremental optimization method grounded in Q-Learning. This strategic adaptation empowers our model to continuously refine its retrieval capabilities when confronted with new data, ensuring sustained efficacy. Our method stands out by preserving small processing delays in compared to current innovative models, making it suitable for real-time applications. Notably, our model effectuates a marked improvement, enhancing precision by 5.9%, recall by 4.3%, and retrieving accuracy by 8.5%. These achievements underscore the pragmatic viability of our approach across a spectrum of image retrieval scenarios. In summation, our innovative hybrid model seamlessly amalgamates EHO, PSO, GA, and Q-Learning. This amalgamation seamlessly navigates feature selection, image ranking, and continual refinement. Thus, it provides a comprehensive and robust solution, effectively surmounting the multifaceted challenges associated with diverse datasets and affirming its prowess as a potent tool for real-time image retrieval applications.
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