Smart adaptive learning is crucial for enhanced image retrieval in the realm of Content-Based Image Retrieval (CBIR), especially with the exponential growth of visual information in the digital age. The challenges in CBIR lie in accurately identifying similarities and dissimilarities between images. To address this, a novel approach called SEGJO (Scaling Factor and Elite Opposition Learning-based Golden Jackal Optimization) has been introduced for effective clustering of extracted features in CBIR.
The SEGJO method incorporates a Scaling Factor (SF) and Elite Opposition Learning (EOL) to enhance search capabilities and prevent premature convergence during the clustering process. This approach is designed to extract key features related to texture, shape, and color using advanced techniques such as Local Binary Pattern, Zernike Moments, and Color Moments.
Additionally, an Entropy-based Divergence (ED) function is integrated into a Convolutional Neural Network (CNN) named EDCNN to improve matching accuracy by reducing redundant activation in hidden layers. The performance of the proposed SEGJO-EDCNN method has been evaluated on the Corel 5 K and Oxford Flower datasets, measuring precision, recall, F-score, mean average precision, and average recall.
Comparative analysis with existing methods such as ELNDP, SVM-CBIR, SPDNN, and DNN-SAR demonstrates that SEGJO-EDCNN achieves higher mean average precision on both datasets, outperforming other techniques. The SEGJO-EDCNN method showcases superior performance in enhancing image retrieval accuracy and efficiency.
Moreover, the study delves into the historical context and significance of adaptive learning in the field of image retrieval, highlighting the importance of innovative approaches like SEGJO-EDCNN for advancing the capabilities of CBIR systems. The integration of adaptive learning techniques and optimized feature clustering represents a significant advancement in the domain of image retrieval, offering valuable insights for future research and development in this area.
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