Book

Content-Based Image Retrieval (CBIR) is a critical technology that has image search applications by enabling users to find visually similar images without relying on textual annotations. Traditional CBIR systems, which are based on handcrafted features, often struggle to capture the intricate and high-level semantics of images, limiting their accuracy and generalization across diverse image datasets. It proposes an advanced CBIR framework that leverages deep learning techniques, specifically Convolutional Neural Networks (CNNs), to automatically extract rich, high-level feature representations from images. By utilizing pre-trained CNN models such as VGG16. The model is trained using augmented image data, enabling robust performance despite limited training samples. Feature extraction is performed using the convolutional base of VGG19, and the classification head is customized for the target dataset. The training process incorporates early stopping based on loss thresholds to prevent overfitting. Performance is evaluated using accuracy and loss metrics for both training and validation sets.