A cluster-based feature selection method for image texture classification
Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2019-06-01.
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| Online Access: | Get fulltext |
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| LEADER | 02994 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs16643_12225 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Alharan, Abbas F. H. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Fatlawi, Hayder K. |e author |
| 700 | 1 | 0 | |a Ali, Nabeel Salih |e author |
| 245 | 0 | 0 | |a A cluster-based feature selection method for image texture classification |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16643 | ||
| 520 | |a Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Data Mining; Image Processing; Clustering; Classification | ||
| 690 | |a Feature selection; Feature extraction; Feature evaluation; K-means clustering; Classification; Image texture features | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 14, No 3: June 2019; 1433-1442 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v14.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16643/12225 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16643/12225 |z Get fulltext |