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Feature Extraction Methods: A Review
Open accessAbstract
Feature extraction is the main core in diagnosis, classification, clustering, recognition, and detection. Many researchers may by interesting in choosing suitable features that used in the applications. In this paper, the most important features methods are collected, and explained each one. The features in this paper are divided into four groups; Geometric features, Statistical features, Texture features, and Color features. It explains the methodology of each method, its equations, and application. In this paper, we made acomparison among them by using two types of image, one type for face images (163 images divided into 113 for training and 50 for testing) and the other for plant images(130 images divided into 100 for training and 30 for testing) to test the features in geometric and textures. Each type of image group shows that each type of images may be used suitable features may differ from other types.
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“Color conversion, such as converting from RGB to an HSV color space [66], can also be beneficial as it helps in distinguishing objects under varying lighting conditions.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [66]
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Cites
Digital Image Processing Using MATLAB
González, R. C., Woods, R. E., & Eddins, S. L. (2003) · Cited by 7,079
Introduction to Machine Learning
(2019) · Series in machine perception and artificial intelligence · doi:10.1142/9789811201967_0001 · Cited by 1,638
Approximate Calculation of Multiple Integrals
G., W., & Stroud, A. H. (1973) · Mathematics of Computation · doi:10.2307/2005635 · Cited by 1,480
Evaluation of Texture Features for Content-Based Image Retrieval
Howarth, P. H., & Rüger, S. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27814-6_40 · Cited by 229
Automatic Detection of Melanoma Skin Cancer using Texture Analysis
Mabrouk, M. S., Sheha, M. A., & Sharawy, A. A. (2012) · International Journal of Computer Applications · doi:10.5120/5817-8129 · Cited by 170
A Computer Based Feature Extraction of Lung Nodule in Chest X-Ray Image
Lingayat, N. S., & Tarambale, M. R. (2013) · International Journal of Bioscience Biochemistry and Bioinformatics · doi:10.7763/ijbbb.2013.v3.289 · Cited by 37
FEATURE EXTRACTION OF MAMMOGRAMS
Pradeep, N., Girisha, H., Sreepathi, B., & Karibasappa, K. G. (2012) · International Journal of Bioinformatics Research · doi:10.9735/0975-3087.4.1.241-244 · Cited by 32
Chest X-ray features extraction for lung cancer classification
Patil, S. A., & Udupi, V. R. (2010) · Journal of Scientific & Industrial Research · Cited by 22
Research works printed but not linked: 6
- Kavya, 2015, Feature Extraction Technique for Robust and Fast Visual Tracking: A Typical Review, International Journal of Emerging Engineering Research and Technology, 3, 98
- Kodituwakku, 2011, Comparison of Color Features for Image Retrieval, Indian Journal of Computer Science and Engineering, 1, 207
- Umamaheswari, 2018, Texture and Color Feature Extraction from Ceramic Tiles for Various Flaws Detection Classification, International Journal on Future Revolution in Computer Science & Communication Engineering, 4, 169
- Ahmad, 2009, Application of texture analysis in the assessment of chest radiograph, International Journal of Video & Image Processing and Network Security (IJVIPNS), 9, 291
- Kaur, 2014, Segmentation and Feature Extraction of Lung Region for the Early Detection of Lung Tumor, International Journal of Science and Research (IJSR), 3
- Aarthy, 2012, Detection of lung nodule using multiscale wavelets and support vector machine, International Journal of Soft Computing and Engineering (IJSCE), 2
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Vovchenko, I. V., Zyablovsky, A. A., Pukhov, A. A., & Andrianov, E. S. (2026) · SciPost Physics Core · doi:10.21468/scipostphyscore.9.3.056
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Hasan, M. M., & Setu, S. S. (2026) · Scientia. Technology, science and society. · doi:10.59324/stss.2026.3(6).14
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Gapski, M. C. B., Kawai, V. A. S., Leticio, G. R., Valem, L. P., Pedronette, D. C. G., & Allili, M. S. (2026) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2026.5880
Kahmei, R. D. R., Neikha, K., Yanthan, S., Pamei, M., Ngullie, N., & Пузари, А. (2026) · Environmental Nanotechnology Monitoring & Management · doi:10.1016/j.enmm.2026.101174
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Metadata
- Authors
- Mutlag, W. K., Ali, S. K., Aydam, Z. M., & Taher, B. H.
- Affiliations
- Southern Technical University (IQ)
- Thi Qar University (IQ)
- University of Sumer (IQ)
- Corresponding author
- Ali, Shaker K.
- Aydam, Zahoor M.
- Venue
- Journal of Physics: Conference Series
- Publisher
- IOP Publishing
- Published
- 2020-07-01
- Volume
- 1591
- Issue
- 1
- Pages
- 012028
- ISSN
- 1742-6588 (ISSN-L)
- 1742-6596
- OpenAlex
- W3049180962
- Cited by
- 257
- References
- 11
- Citations by year
- 2026: 32 · 2025: 72 · 2024: 71 · 2023: 46 · 2022: 25 · 2021: 11
- FWCI
- 6.32
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Physics Conference Series (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Keywords
- feature extraction, geometric features, statistical features, texture features, color features, face images, plant images, feature comparison, image classification
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