Healthcare is a high-priority sector where people expect the highest levels of care and
service, regardless of cost. That makes it distinct from other sectors. Due to the promising …

The “semantic gap” issue which exists the low-level image pixels captured by machines and
high-level semantic concepts perceived by human has always been a key challenge for lots …

Image Retrieval Systems (IRSs) are applications that allow one to retrieve images saved at
any location on a network. Most IRSs make use of reverse lookup to find images stored on …

In a world that seeks perfect results of any search query, an information retrieval system that
produces an accurate and relevant output is desired. However, because of the famous …

The term Content-based image retrieval (CBIR) is widely used to describe the process of
retrieving desired images from a large database on the basis of features that can be …

ABSTRACT A smart image retrieval technique has been an increasing demand by the
advancements in the field of computer networks and mobile computing. Generally, uploaded …

Deep learning with convolutional neural networks (CNNs) is very powerful for image
analysis. Of late, deep architectures in machine learning and artificial intelligence (AI) have …

The use of Convolution neural networks (CNN) with deep learning performed an excellent
performance in many applications of image processing. The use of CNN based techniques …

Abstract In recent years, Scalable Vocabulary Tree (SVT) has been shown to be effective in
image retrieval. However, for general images where the foreground is the object to be …

We propose a novel, efficient, modular and scalable framework for content based visual
media retrieval systems by leveraging the power of Deep Learning which is flexible to work …