• Kotha Tejasri
• Dr. Jhansi Rani Singothu

Secure image retrieval has attracted considerable interests recently due to users security concerns, such as data encryption, feature extraction, and image similarity scoring. CBIR system utilizes the outline of the content present in the image to spot and retrieve related images. A database is a collection of information that is organized for easy storage, retrieval and update. This information is represented in many forms like text, table, image, chart and graph etc. here we concentrate on information that is stored in the form of images. A CBIR system is able to query an image over a database of images and return a set of results that most closely matches the queried image. In this paper, I propose and implement a secure CBIR framework that performs image retrieval on the database. A pre-trained deep CNN model, i.e., VGG-16, is used to extract the deep features of an image. I implemented this CBIR system using python language and flask framework and torch library for better accuracy and efficiency. The experimental results show that the framework is efficient and accurate.

Content based image retrieval (CBIR), Convolution Neural Network model (CNN), VGG-16

"Secure Content Based Image Retrieval System Using CNN and VGG-16", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 11, page no.842-848, November-2020, Available :http://www.jetir.org/papers/JETIR2011110.pdf

"Secure Content Based Image Retrieval System Using CNN and VGG-16", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp842-848, November-2020, Available at : http://www.jetir.org/papers/JETIR2011110.pdf