A VIGOROUS AND OPERATIVE CONTENT BASED IMAGE RETRIEVAL USING ENSEMBEL LEARNING MODELS

Abstract

Content-Based Image Retrieval (CBIR) plays a vital role in modern image processing and computer vision by enabling efficient retrieval of relevant images based on visual content such as color, texture, and shape features. Traditional CBIR systems often suffer from low retrieval accuracy and inefficiency due to feature variability and noise in complex image datasets. To overcome these limitations, this study presents a vigorous and operative CBIR system using Ensemble Learning Models that integrates multiple machine learning algorithms to enhance retrieval performance. The proposed method involves three major stages: (1) Feature Extraction, where discriminative features are derived using color histograms, Local Binary Patterns (LBP), and Histogram of Oriented Gradients (HOG); (2) Feature Optimization and Dimensionality Reduction, performed using Principal Component Analysis (PCA) to eliminate redundant information and improve computational efficiency; and (3) EnsembleBased Classification and Retrieval, where models such as Random Forest, Gradient Boosting, and Support Vector Machines (SVM) are combined using weighted voting or stacking to generate robust retrieval predictions. Experimental results on benchmark image datasets demonstrate that the proposed ensemble-based CBIR system achieves superior precision, recall, and retrieval accuracy compared to conventional single-model methods. The system also shows strong resilience against image distortion and intraclass variations. Overall, the proposed method offers a vigorous, efficient, and scalable framework for intelligent image retrieval, making it highly suitable for real-world applications such as medical imaging, digital libraries, and multimedia content management.