Content Based Image Retrieval Using Deep Learning Convolutional Neural Network

• Published in: IOP conference series. Materials Science and Engineering 2021-03, Vol.1084 (1), p.12026
• Main Authors: Simran, Arshiya, Shijin Kumar, P.S, Bachu, Srinivas
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Deep learning; Digital imaging; Feature extraction; Image classification; Image management; Image processing; Image retrieval; Information retrieval; Machine learning; Neural networks; Search engines; User satisfaction
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/1084/1/012026

Content-based image retrieval (CBIR) is a widely used method for image retrieval from large and unlabeled image collections. However, users are not satisfied with the traditional methods of retrieving information. Moreover the abundance of online networks for production and distribution, as well as the quantity of images accessible to consumers, continues to expand. Therefore, in many areas, permanent as well as widespread digital image processing takes place. Therefore, the rapid access to these large image databases as well as the extraction of identical images from this large set of images from a given image (Query) pose significant challenges as well as involves efficient techniques. A CBIR system’s efficiency depends fundamentally on the calculation of feature representation as well as similarity. For this purpose, they present a basic but powerful deep learning system focused on Convolutional Neural Networks (CNN) and composed of feature extraction and classification for fast image retrieval. We get some promising findings from many detailed observational studies for a number of CBIR tasks using image database, which reveals some valuable lessons for improving the efficiency of CBIR. CBIR systems allow another image dataset to locate related images to such a query image. The search per picture function of Google search has to be the most popular CBIR method.