Review of image low-level feature extraction methods for content-based image retrieval

Sensor Review ◽  
2019 ◽  
Vol 39 (6) ◽  
pp. 783-809
Author(s):  
Shenlong Wang ◽  
Kaixin Han ◽  
Jiafeng Jin
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Extraction Methods ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Image Feature Extraction ◽  
Content Type ◽  
Low Level ◽  
Feature Expression ◽  
Color Texture

Purpose In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years. Design/methodology/approach First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared. Findings The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR. Originality/value A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

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.

The Imapct of Low-Level Features in Semantic-Based Image

2011 ◽  
pp. 23-45 ◽  
Author(s):  
Konstantinos Konstantinidis ◽  
Antonios Gasteratos ◽  
Ioannis Andreadis
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Experimental Results ◽  
Image Feature ◽  
Statistical Tool ◽  
Image Feature Extraction ◽  
Low Level ◽  
High Level

Image Retrieval (IR) is generally known as a collection of techniques for retrieving images on the basis of features, either low-level (Content-based IR) or high-level (Semantic-based IR). Since Semantic-based features rely on low-level ones, in this chapter the reader is initially familiarized with the most widely used low-level features. An efficient way to present these features is by means of a statistical tool capable of bearing concrete information, such as the histogram. For use in IR, the histograms extracted from the previously mentioned features need to be compared by means of a metric. The most popular methods and distances are, thus, apposed. Finally, a number of IR systems using histograms are presented in a thorough manner and their experimental results are discussed. The steps in order to develop a custom IR system, along with modern techniques in image feature extraction are also presented.

scholarly journals Content Based Image Retrieval using Texture, Color and Shape for Image Analysis

2012 ◽  
Vol 3 (1) ◽  
pp. 149-152 ◽  
Author(s):  
Amanbir Sandhu ◽  
Aarti Kochhar
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Extraction Techniques ◽  
Low Level ◽  
The Past ◽  
Single Feature ◽  
Color Texture

Content- Based Image Retrieval(CBIR) or QBIR  is the important  field of research..Content  Based Image retrieval has gained much popularity  in the past Content-based image retrieval (CBIR)[1] system has also helped users to retrieve relevant images based on their contents. It represents low level features like texture ,color and shape .In this paper, we compare the several feature extraction techniques [5]i.e..GLCM ,Histogram and shape properties  over color,  texture and shape The experiments show the similarity between these features and also that the output obtained using this combination of color, texture and shape is better as obtaining output  with a single feature

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.

scholarly journals Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

scholarly journals COLOR FEATURE WITH SPATIAL INFORMATION EXTRACTION METHODS FOR CBIR: A REVIEW

2019 ◽  
Vol 45 (1) ◽  
pp. 15-19
Author(s):  
Sarmad Abdul-samad
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Information Extraction ◽  
Spatial Information ◽  
Extraction Methods ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Visual Content ◽  
Color Feature ◽  
Color Correlogram

Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for theresearchers. It depending one analysis of the image’s visual content which can be done by extracting the color, texture and shapefeatures. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color featureis the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely LocalColor Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors featurestaking in consideration the spatial information of the image.

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