Deep Learning Transformations in Content-Based Image Retrieval: Exploring the Visual Geometry Group (VGG16) Model

Content-based image retrieval (CBIR) is evolving with new computational paradigms such as deep learning in vision applications viz. visual searches, machine translation, and query-based image retrieval. The deep visual learning with Visual Geometry Group (VGG) Model is one of the support layer-based convolution neural networks (CNN) utilized in multiple machine vision tasks like image segmentation, categorization, and object detection within image recovery systems. A VGG architecture consists of numerous blocks, each having a maximum-pooling layer, bus encoder, and 2D complexity. VGG16 consists of 16 layers having extracting features and learning weights to enhance the system's performance. This highlights the challenge of handling large datasets, compounded by the complexity of the network's architecture and resulting features.