Content-Based Image Retrieval Using Deep Learning

Abstract

The most prevalent and well-used method for obtaining images from huge, unlabelled image datasets is content-based image retrieval. Convolutional Neural Networks are pre-trained deep neural networks which can generate and extract accurate features from image databases. These CNN models have been trained using large databases with thousands of classes that include a huge number of images, making it simple to use their information. Based on characteristics retrieved using the pre-trained CNN models, we created CBIR systems in the work. These pre-trained CNN models VGG16, and MobileNet have been employed in this instance to extract sets of features that are afterward saved independently and used for image retrieval.