The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

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 …

In the recent years the rapid growth of multimedia content makes the image retrieval a
challenging research task. Content Based Image Retrieval (CBIR) is a technique which uses …

Content-based image retrieval (CBIR) uses image content features to search and retrieve
digital images from a large database. A variety of visual feature extraction techniques have …

In this paper we propose a model retraining method for learning more efficient convolutional
representations for Content Based Image Retrieval. We employ a deep CNN model to obtain …

Day by day, all the research communities have been focusing on digital image retrieval due
to more internet and social media uses. In this paper, a U‐Net‐based neural network is …

Deep learning for image retrieval has been used in this era, but image retrieval with the
highest accuracy is the biggest challenge, which still lacks auto-correlation for feature …

Deep convolutional neural networks have been successfully applied to image classification
tasks. When these same networks have been applied to image retrieval, the assumption has …

This paper presents an effective image retrieval method by combining high-level features
from convolutional neural network (CNN) model and low-level features from dot-diffused …

Nowadays, the use of Convolutional Neural Networks (CNNs) has led to tremendous
achievements in several computer vision challenges. CNN-based image retrieval methods …