An enhanced CBIR System Using Modified VGG16 ‎with Stacked SVM, Random Forest, and XGBoost ‎ ‎Classifiers

CBIR systems have improved large image dataset search and management, yet traditional techniques with HSV GLCM and SIFT experience ‎limited precision because of their insufficient feature extraction abilities. The research requires additional efforts to boost retrieval precision and ‎the overall computer vision model functioning, specifically for CNN-SVM constructions. Through VGG16-based feature extraction, a newly ‎proposed hybrid Content-Based Image Retrieval framework incorporates a stacked classification system built using SVM alongside RF and ‎XGBoost for decision-making. Tests on the Wang dataset validated this model by showing the CNN-SVM baseline model delivering precision ‎levels of 83.61% at 10 retrievals, and both 83.67% at 15 and 83.37% at 20 retrievals. Implementing stacked classifiers produced advanced ‎decision boundaries that improved classification performance while raising total precision by 12.06% at all retrieval settings above the baseline ‎model. The research work establishes the basic groundwork for next-generation hybrid CBIR approaches while demonstrating the benefits of ‎deep learning and ensemble technologies in improving retrieval performance.