Content Based Image Retrieval Using Ensemble Learning Method Title

Abstract

Introduction: The availability of Internet technologies and the low cost of digital image sensors have led to the creation of vast amounts of image databases for various application fields. These image databases have increased the demand for developing efficient image retrieval methods to meet users' needs. A great deal of attention and effort has been devoted to improving content-based image retrieval methods, especially focusing on reducing the semantic gap between simple features and human visual recognition.

Objectives: To develop and implement a Content-Based Image Retrieval (CBIR) system enhanced by ensemble machine learning techniques, aiming to improve retrieval accuracy and reduce the semantic gap between low-level image features and high-level human visual understanding.

Methods: In this study, we employed an ensemble machine learning approach to enhance the performance of Content-Based Image Retrieval (CBIR). Initially, image features were extracted using the pre-trained VGG16 model, leveraging its deep convolutional layers to capture rich spatial features. These features were further refined using a custom Convolutional Neural Network (CNN) to learn task-specific patterns. The extracted features were then flattened and passed into the XGBoost classifier for robust prediction and categorization. By combining the strengths of deep learning (VGG16, CNN) with the gradient boosting framework (XGBoost), the ensemble model achieves superior retrieval accuracy. This hybrid method effectively narrows the semantic gap and enhances image similarity detection. The ensemble approach demonstrated up to 99% prediction accuracy on our test dataset.

Results: In this paper, a study has been carried out on feature extraction using VGG16 with XGBoost classifier. From this experiment we can conclude that combination of VGG16 & XGBoost technique gives very good results of query image and retrieved images resulting with high accuracy. In addition, XGBoost is optimized to make the structure of the model better match the extracted features, so as to better understand the image features.

Conclusions: XGBoost is Boosting: combines multiple weak learners (decision trees) to form a strong predictive model. Regularization: Includes techniques to prevent overfitting, enhancing model generalization. Efficiency: optimized for speed and performance, making it suitable for large datasets. Flexibility: Supports various objective functions and evaluation metrics. Also, the accuracy of the model is very high.