Image retrieval is a challenging problem in computer vision domain. Traditional content
based image retrieval (CBIR) systems were built to retrieve images based on low level …

Content-based image retrieval (CBIR) is a widely used technique for retrieval images from
huge and unlabeled image databases. However, users are not satisfied with the traditional …

Content based image retrieval (CBIR) systems is a common recent method for image
retrieval and is based mainly on two pillars extracted features and similarity measures. Low …

Today, with the rapid growth of communication technology and the development of social
networks and smartphones, the amount of data stored by users in the form of images has …

With the growing popularity of digital services and internet technology billions of people are
prone to information sharing and uploading photos. For accurate retrieval of images from …

In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in
Content Based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic …

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 …

Searching a collection of images that have similarities with input images, without knowing
the name of the image, makes a search system that applies the concept of content-based …

A content-based image retrieval (CBIR) system works on the low-level visual features of a
user input query image, which makes it difficult for the users to formulate the query and also …

Images can communicate a service, brand or product. Moreover, images provide depth and
context to a description or story and give a much more intense experience than writing …