Content based image retrieval using deep learning process

2018 ◽  
Vol 22 (S2) ◽  
pp. 4187-4200 ◽  
Author(s):  
R. Rani Saritha ◽  
Varghese Paul ◽  
P. Ganesh Kumar
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Learning Process ◽  
Content Based Image Retrieval

scholarly journals Deep Learning for Content-Based Image Retrieval

2014 ◽  
Author(s):  
Ji Wan ◽  
Dayong Wang ◽  
Steven Chu Hong Hoi ◽  
Pengcheng Wu ◽  
Jianke Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Content Based Image Retrieval

scholarly journals A Biomorphic Model of Cortical Column for Content—Based Image Retrieval

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1458
Author(s):  
Alexander Telnykh ◽  
Irina Nuidel ◽  
Olga Shemagina ◽  
Vladimir Yakhno
Keyword(s):  
Image Processing ◽  
Information Processing ◽  
Receptive Field ◽  
Image Retrieval ◽  
Learning Process ◽  
Functional Unit ◽  
Living Systems ◽  
Content Based Image Retrieval ◽  
Functional Modules ◽  
Cortical Column

How do living systems process information? The search for an answer to this question is ongoing. We have developed an intelligent video analytics system. The process of the formation of detectors for content-based image retrieval aimed at detecting objects of various types simulates the operation of the structural and functional modules for image processing in living systems. The process of detector construction is, in fact, a model of the formation (or activation) of connections in the cortical column (structural and functional unit of information processing in the human and animal brain). The process of content-based image retrieval, that is, the detection of various types of images in the developed system, reproduces the process of “triggering” a model biomorphic column, i.e., a detector in which connections are formed during the learning process. The recognition process is a reaction of the receptive field of the column to the activation by a given signal. Since the learning process of the detector can be visualized, it is possible to see how a column (a detector of specific stimuli) is formed: a face, a digit, a number, etc. The created artificial cognitive system is a biomorphic model of the recognition column of living systems.

Content-Based Image Retrieval Using Deep Learning

2021 ◽  
pp. 771-785
Author(s):  
Tristan Jordan ◽  
Heba Elgazzar
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Content Based Image Retrieval

A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

2019 ◽  
pp. 54-64
Author(s):  
Mohamed Elsharkawy ◽  
◽  
Ahmed N. Al Masri ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Image Encryption ◽  
Cuckoo Search ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Search Optimization ◽  
Proposed Model ◽  
Cuckoo Search Optimization ◽  
Visual Properties

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.

scholarly journals Content-Based Image Retrieval : A Comprehensive Study

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.

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