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.

Study and Analysis of User Desired Image Retrieval

2020 ◽  
Vol 13 ◽  
Author(s):  
John Bosco P ◽  
S Janakiraman
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Image Retrieval ◽  
State Of The Art ◽  
The State ◽  
Content Based Image Retrieval ◽  
Processing Application ◽  
Detection Techniques ◽  
Digital World ◽  
Color Texture

Background: In the present digital world, Content Based Image Retrieval (CBIR) has gained significant importance. In this context, the image processing technology has become the most sought one, as a result its demand has increased to a large extend. The complex growth concerning computer technology offers a platform to apply the image processing application. Well-known image retrieval techniques suitable for application zone are 1.Text Based Image Retrieval (TBIR) 2. Content Based Image Retrieval (CBIR) and 3.Semantic Based Image Retrieval (SBIR) etc. In recent past, many researchers have conducted extensive research in the field of content-based image retrieval (CBIR). However, many related research studies on image retrieval and characterization have exemplified to be an immense issue and it should be progressively developed in its techniques. Hence, by putting altogether the research conducted in the recent years, this survey study makes a comprehensive attempt to review the state-of –the art in the field. Aims: This paper aims to retrieve similar images according to visual properties, which defined as Shape, color, Texture and edge detection. Objective: To investigate the CBIR to achieve the task because of the essential and fundamentals problems. The present and future trends are addressed to show come contributions and directions and it can inspire more research in the CBIR methods. Result: we present a deep analysis of the state of the art on CBIR methods; we explain the methods based on Color, Texture, and shape, and edge detection with performance evaluation metrics. In addition, we have discussed some significant future research directions reviewed. Methods: This paper has quickly anticipated the noteworthiness of CBIR and its related improvement, which incorporates Edge Detection Techniques, Various sorts of Distance Metric (DM), Performance measurements and various kinds of Datasets. This paper shows the conceivable outcomes to overcome the difficulties concerning re-positioning strategies with an exceptional spotlight on the improvement of accuracy and execution. Discussion: At last, we have proposed another technique for consolidating different highlights in a CBIR framework that can give preferred outcomes over the current strategies.

Radial Moments for Image Retrieval

2017 ◽  
pp. 358-389
Author(s):  
Pooja Sharma
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Performance Measures ◽  
Similarity Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Visual Data ◽  
Image Feature Extraction ◽  
Radial Moments

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

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 Feature Fusion for Image Retrieval using Image Processing

2019 ◽  
Vol 8 (12) ◽  
pp. 3157-3162
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Retrieval ◽  
Principle Component Analysis ◽  
Clustering Algorithm ◽  
Feature Fusion ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Huge Amount ◽  
Attribute Data

Image processing and computer vision uses Content-based image retrieval (CBIR) function to solve the issue of image retrieval, which means, solving the issue of image searching in expansive databases. The actual data of the image will be evaluated when a search is performed that refers to content-based. The term content can be any attribute of an image like colour-shade, various symbols or shapes, sizes, or any other data. There are various approaches for image retrieval but the most prominent are by comparing the main image with the subsets of the relatable images whether it matches or not and the other one is by using a matching descriptor for the image. One of the main trouble for huge amount of CBIR is the representation of an image. When a given image is worked upon it is divided into number of attributes in which some are the primary ones and others are the secondary ones. These attributes are checked with the local and MPEG-7 descriptors. All this is then mapped in a single vector which is the same images but in compact form to save the space. Principle Component Analysis (PCA) is used lessen the attribute size. To store the attribute data in similar clusters and to train them to give the correct output the study also uses k-means clustering algorithm. Hence, the proposed system deals with the image retrieval using various algorithms and methods.

scholarly journals LApIS: developing research applied to health care area

2011 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Fátima L S Nunes ◽  
Helton H Bíscaro ◽  
Márcio E Delamaro ◽  
Romero Tori ◽  
Ricardo Nakamura
Keyword(s):  
Image Processing ◽  
Health Care ◽  
Virtual Reality ◽  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
History Of ◽  
Application Scope

The Laboratory of Computer Applications for Health Care is a Brazilian lab researching in the virtual reality, Content-based image retrieval, and image processing areas, mainly developing applications to the health care area, although many of the techniques and tools extrapolate this application scope. . In this paper we present a brief history of the laboratory, its mission as well as current projects and collaborations. All the projects developed at LApIS have some relevant aspects for researches in the graphics area, which can become new opportunities for student’s integration and new collaborations.

Radial Moments for Image Retrieval

2018 ◽  
pp. 2420-2451
Author(s):  
Pooja Sharma
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Performance Measures ◽  
Similarity Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Visual Data ◽  
Image Feature Extraction ◽  
Radial Moments

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

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