Content-Based Image Retrieval Using Deep Learning

From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over the other methods.

Download Full-text

Deep learning feature map for content based image retrieval system for remote sensing application

International Journal of Image Mining ◽

10.1504/ijim.2016.079113 ◽

2016 ◽

Vol 2 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Swati Jain ◽

Tanish Zaveri ◽

Kinjal Prajapati ◽

Shailee Patel

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Image Retrieval ◽

Retrieval System ◽

Content Based Image Retrieval ◽

Remote Sensing Application ◽

Feature Map ◽

Image Retrieval System ◽

Sensing Application

Download Full-text

Secure content based image retrieval system using deep learning with multi share creation scheme in cloud environment

Multimedia Tools and Applications ◽

10.1007/s11042-021-10998-7 ◽

2021 ◽

Author(s):

R. Punithavathi ◽

A. Ramalingam ◽

Chinnarao Kurangi ◽

A. Siva Krishna Reddy ◽

J. Uthayakumar

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Retrieval System ◽

Content Based Image Retrieval ◽

Cloud Environment ◽

Image Retrieval System

Download Full-text

Content-Based Image Retrieval : A Comprehensive Study

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952275 ◽

2019 ◽

pp. 1073-1081

Author(s):

Er Aman ◽

Amit Rawat ◽

Ashwin Giri ◽

Hardik Gothwal

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Empirical Studies ◽

Content Based Image Retrieval ◽

Recent Success ◽

Long Run ◽

Learning Techniques ◽

Depth Analysis ◽

High Level ◽

Learning Options

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.

Download Full-text