Overview of Content-based Image Feature Extraction Methods

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.

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Improving viewpoint invariance of image feature extraction methods using intensity and range images

Proceedings of the 13th International Carpathian Control Conference (ICCC) ◽

10.1109/carpathiancc.2012.6228670 ◽

2012 ◽

Author(s):

V. Kovacs ◽

G. Tevesz

Keyword(s):

Feature Extraction ◽

Extraction Methods ◽

Image Feature ◽

Range Images ◽

Image Feature Extraction ◽

Viewpoint Invariance

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Fault diagnosis of rotating machinery based on time-frequency image feature extraction

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189004 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5193-5200

Author(s):

Shiyi Zhang ◽

Laigang Zhang ◽

Teng Zhao ◽

Mahmoud Mohamed Selim

Keyword(s):

Feature Extraction ◽

Local Binary Pattern ◽

Texture Features ◽

Extraction Methods ◽

Image Feature ◽

Simulation Data ◽

Image Feature Extraction ◽

Feature Extraction Method ◽

Time Frequency ◽

Better Than

Aiming at the characteristics of time-frequency analysis of unsteady vibration signals, this paper proposes a method based on time-frequency image feature extraction, which combines non-downsampling contour wave transform and local binary mode LBP (Local Binary Pattern) to extract the features of time-frequency image faults. SVM is used for classification and recognition. Finally, the method is verified by simulation data. The results show that the classification accuracy of the method reaches 98.33%, and the extracted texture features are relatively stable. Also, the method is compared with the other 3 feature extraction methods. The results also show that the classification effect of the method is better than that of the traditional feature extraction method.

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New Feature Extraction Method Based on Contourlet Transform for Banknote Classification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.321-324.1061 ◽

2013 ◽

Vol 321-324 ◽

pp. 1061-1065

Author(s):

Guo Wei Yang ◽

Wen Ling Wang ◽

Shan Gai

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Extraction Methods ◽

Image Feature ◽

Contourlet Transform ◽

Image Texture ◽

Statistical Characteristics ◽

Image Feature Extraction ◽

Feature Extraction Method ◽

New Feature

In order to improve the performance of the banknote classification, new banknote image feature extraction method is proposed in this paper. The contourlet transform is applied to the original banknote image which is obtained by image contact sensor.The statistical characteristics of transformed image in the contourlet domain are analyzed. The statistical characteristics which can perfectly reflect the banknote image texture information are used as feature vector for banknote classification. The experimental results show that the proposed method can obtain higher recognition compared with other conventional banknote image feature extraction methods.

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