scholarly journals Image retrieval techniques: a survey

2017 ◽  
Vol 7 (1.2) ◽  
pp. 215 ◽  
Author(s):  
Aman Dureja ◽  
Payal Pahwa
Keyword(s):  
Image Retrieval ◽  
Medical Systems ◽  
Digital Repositories ◽  
Advantages And Disadvantages ◽  
Retrieval Systems ◽  
Large Databases ◽  
Base Method ◽  
Efficient Retrieval ◽  
Similar Images ◽  
Large Image Databases

In the recent years, the development in computer technologies and multimedia applications has led to the production of huge digital images and large image databases, and it is increasing rapidly. There are several different areas in which image retrieval plays a crucial role like Medical systems, Forensic Labs, Tourism Promotion, etc. Thus retrieval of similar images is a challenge. To tackle this rapid growth in digital repositories it is necessary to develop image retrieval systems, which can operate on large databases. There are basically three techniques, which is useful for efficient retrieval of images. With these techniques, the number of methods has been modified for the efficient image retrieval of images. In this paper, we presented the survey of different techniques that has been used starting from Image retrieval using visual features and latest by the deep learning with CNN that contains the number of layers and now becomes the best base method for retrieval of images from the large databases. In the last section, we have made the analysis between various developed techniques and showed the advantages and disadvantages of various techniques.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 281-302
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

Interactive Visualization Tool for Analysis of Large Image Databases

2011 ◽  
pp. 266-281
Author(s):  
Anca Doloc-Mihu
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Effective Interaction ◽  
Qualitative Evaluation ◽  
Image Databases ◽  
Multiple Views ◽  
End User ◽  
Retrieval Systems ◽  
Image Retrieval System ◽  
Large Image Databases

Navigation and interaction are essential features for an interface that is built as a help tool for analyzing large image databases. A tool for actively searching for information in large image databases is called an Image Retrieval System, or its more advanced version is called an Adaptive Image Retrieval System (AIRS). In an Adaptive Image Retrieval System (AIRS) the user-system interaction is built through an interface that allows the relevance feedback process to take place. In this chapter, the author identifies two types of users for an AIRS: a user who seeks images whom the author refers to as an end-user, and a user who designs and researches the collection and the retrieval systems whom the author refers to as a researcher-user. In this context, she describes a new interactive multiple views interface for an AIRS (Doloc-Mihu, 2007), in which each view illustrates the relationships between the images from the collection by using visual attributes (colors, shapes, proximities). With such views, the interface allows the user (both end-user and researcher-user) a more effective interaction with the system, which, further, helps during the analysis of the image collection. The author‘s qualitative evaluation of these multiple views in AIRS shows that each view has its own limitations and benefits. However, together, the views offer complementary information that helps the user in improving his or her search effectiveness.

scholarly journals Retrieval of Images using Combination of Features as Color, Color Moments and Hu Moments

2019 ◽  
Vol 7 (5) ◽  
pp. 09-21
Author(s):  
RAJKUMAR RAJ ◽  
Dr. M V Sudhamani
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Retrieval Performance ◽  
Recall Accuracy ◽  
Hsv Color Space ◽  
Low Level ◽  
Digital Era ◽  
Retrieval Systems ◽  
Hu Moments ◽  
Similar Images

In today’s digital era, several of the image retrieval systems focus on retrieving images using features from images themselves such as color, shape and textures and are referred as low-level features. In this proposed work, the features like color with HSV color space, color moments and Hu moments are employed to retrieve similar images. Various experimentations were conducted on Wang’s database images to test the combination of features for higher performance using precision, recall, accuracy and f-score. The results obtained are compared with one another and also with existing works. The retrieval performance is found to be high for proposed system against existing works.

A Content-Based Approach to Medical Image Database Retrieval

2009 ◽  
pp. 1062-1083
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 258-292 ◽  
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

A Hierarchial Classification Technique for Semantics-Based Image Retrieval

2011 ◽  
pp. 311-333 ◽  
Author(s):  
Mohammed Lamine Kherfi ◽  
Djemel Ziou
Keyword(s):  
Image Retrieval ◽  
Retrieval Accuracy ◽  
The Third ◽  
Retrieval Systems ◽  
Probabilistic Classification ◽  
Semantic Concepts ◽  
Similar Images ◽  
Hierarchical Index ◽  
Upper Level ◽  
Abstraction Levels

We present a new approach for improving image retrieval accuracy by integrating semantic concepts. First, images are represented according to different abstraction levels. At the lowest level, they are represented with visual features. At the upper level, they are represented with a set of very specific keywords. At the subsequent levels, they are represented with more general keywords. Second, visual content together with keywords are used to create a hierarchical index. A probabilistic classification approach is proposed, which allows to group similar images into the same class. Finally this index is exploited to define three retrieval mechanisms: the first is text-based, the second is content-based, and the third is a combination of both. Experiments show that our combination allows to nicely narrow the semantic gap encountered by most current image retrieval systems. Furthermore, we show that the proposed method helps reducing retrieval time and improving retrieval accuracy.

scholarly journals Region of interest-based image retrieval techniques: a review

2020 ◽  
Vol 9 (3) ◽  
pp. 520
Author(s):  
Mardhiyah Md Jan ◽  
Nasharuddin Zainal ◽  
Shahrizan Jamaludin
Keyword(s):  
Image Retrieval ◽  
Region Of Interest ◽  
Reference Source ◽  
Global Features ◽  
Retrieval Systems ◽  
Large Databases ◽  
Indexing And Retrieval ◽  
Image Retrieval System ◽  
Specific Interests ◽  
Comprehensive Reference

<span lang="EN-US">This paper presents a review of the region of interest-based (ROI) image retrieval techniques. In this study, the techniques, the performance evaluation parameters, and databases used in image retrieval process are being reviewed. A part of an image that is considered important or a selected certain area of the image is what defines a region of interest. Retrieval performance in large databases can be improved with the application of content-based image retrieval systems which deals with the extraction of global and region features of images. The capability of reflecting users' specific interests with greater accuracy has shown to be more effective when using region-based features compared to global features. Segmentation, feature extraction, indexing, and retrieval of an image are the tasks required in retrieving images that contain similar regions as specified in a query. The idea of the region of interest-based image retrieval concepts is presented in this paper and it is expected to accommodate researchers that are working in the region-based image retrieval system field. This paper reviews the work of image retrieval researchers in the span of twenty years. The main goal of this paper is to provide a comprehensive reference source for scholars involved in image retrieval based on ROI.</span>

scholarly journals Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

2021 ◽  
Vol 8 (1) ◽  
pp. 110-116
Author(s):  
Dannina Kishore ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Feature Fusion ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Single Feature ◽  
Image Retrieval System ◽  
Efficient Retrieval ◽  
Multiple Resolutions ◽  
Similar Images

In the last few years, Content-Based Image Retrieval (CBIR) has received wide attention. Compared to text-based image retrieval contents of the image are more in information for efficient retrieval by Content-Based Image Retrieval. The single feature cannot be applied to all the images and provides lower performance. In this paper, we have put forward a proposal on an image retrieval using multi-feature fusion. The concept of multi-resolution has been exploited with the help of a wavelet transform. This method combines Local Binary Pattern (LBP) with Fast and Accurate Exponent Fourier Moments (FAEFM’s) with the wavelet decomposition of an image using multiple resolutions. In order to extract the feature of texture from image, LBP codes of Discrete Wavelet Transform (DWT), the image coefficients are estimated followed by the computation of Fast and Accurate Exponent Fourier Moments to these LBP codes so as to extract features of shape to construct the required feature vector. These constructed vectors aid us in exactly finding out and retrieving visually similar images from existing databases. The benchmark databases Corel-1k and Olivia 2688 are used to test the proposed method. The proposed method achieves 99.99% of precision and 93.15% of recall on Corel-1k database and 99.99% of precision and recall of 93.63% on Olivia-2688 database, which are higher than the existing methods.

scholarly journals Comparison of query optimization techniques in content based image retrieval (CBIR)

2019 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Mariam Alharthi ◽  
Fahad Alqurashi
Keyword(s):  
Information Systems ◽  
Image Retrieval ◽  
Query Optimization ◽  
World Wide ◽  
Optimization Techniques ◽  
Content Based Image Retrieval ◽  
Retrieval Process ◽  
Digital Contents ◽  
Efficient Retrieval ◽  
Similar Images

With increasing the popularity of World Wide Web, storing digital contents increases enormously, in that case, it is important to implement convenient information systems which manage the collections of these digital contents efficiently. This paper concentrates on hastening techniques for efficient retrieval of images. Content-Based Image Retrieval (CBIR) systems are used by common approaches. These systems support retrieving similar images depend on content properties (e.g., color, shape, and texture) by retrieving automatically similar images to a pattern or user-defined specification. The CBIR generally used in several applications by applying different techniques in each application which in turns enhance the retrieval process. The paper aims to evaluate some of these applications and compare them to find out the proper methods that return the best results in these CBIR systems.

scholarly journals An Appraisal of Content-Based Image Retrieval (CBIR) Methods

2019 ◽  
pp. 1-15
Author(s):  
J. O. Olaleke ◽  
A. O. Adetunmbi ◽  
B. A. Ojokoh ◽  
Iroju Olaronke
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Digital Libraries ◽  
Large Scale ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Advantages And Disadvantages ◽  
Feature Extraction And Selection ◽  
Similar Images ◽  
Google Search

Background: Content Based Image Retrieval (CBIR) is an aspect of computer vision and image processing that finds images that are similar to a given query image in a large scale database using the visual contents of images such as colour, texture, shape, and spatial arrangement of regions of interest (ROIs) rather than manually annotated textual keywords. A CBIR system represents an image as a feature vector and measures the similarity between the image and other images in the database for the purpose of retrieving similar images with minimal human intervention. The CBIR system has been deployed in several fields such as fingerprint identification, biodiversity information systems, digital libraries, Architectural and Engineering design, crime prevention, historical research and medicine. There are several steps involved in the development of CBIR systems. Typical examples of these steps include feature extraction and selection, indexing and similarity measurement. Problem: However, each of these steps has its own method. Nevertheless, there is no universally acceptable method for retrieving similar images in CBIR. Aim: Hence, this study examines the diverse methods used in CBIR systems. This is with the aim of revealing the strengths and weakness of each of these methods. Methodology: Literatures that are related to the subject matter were sought in three scientific electronic databases namely CiteseerX, Science Direct and Google scholar. The Google search engine was used to search for documents and WebPages that are appropriate to the study. Results: The result of the study revealed that three main features are usually extracted during CBIR. These features include colour, shape and text. The study also revealed that diverse methods that can be used for extracting each of the features in CBIR. For instance, colour space, colour histogram, colour moments, geometric moment as well as colour correlogram can be used for extracting colour features. The commonly used methods for texture feature extraction include statistical, model-based, and transform-based methods while the edge method, Fourier transform and Zernike methods can be used for extracting shape features. Contributions: The paper highlights the benefits and challenges of diverse methods used in CBIR. This is with the aim of revealing the methods that are more efficient for CBIR. Conclusion: Each of the CBIR methods has their own advantages and disadvantages. However, there is a need for a further work that will validate the reliability and efficiency of each of the method.

Sign in / Sign up

Export Citation Format

Share Document