Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix
In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was u...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Baghdad
2023-01-01
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| Series: | Ibn Al-Haitham Journal for Pure and Applied Sciences |
| Subjects: | |
| Online Access: | https://jih.uobaghdad.edu.iq/index.php/j/article/view/2894 |
| _version_ | 1797937705283223552 |
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| author | Haider S. Kaduhm Hameed M. Abduljabbar |
| author_facet | Haider S. Kaduhm Hameed M. Abduljabbar |
| author_sort | Haider S. Kaduhm |
| collection | DOAJ |
| description | In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between every Two cases or two steps (two different angles and for the same number of classes). The agreement percentage between the classification results and the various methods was calculated. |
| first_indexed | 2024-04-10T18:48:29Z |
| format | Article |
| id | doaj.art-a91c0cf6a95c44119697ee082eaf0da0 |
| institution | Directory Open Access Journal |
| issn | 1609-4042 2521-3407 |
| language | English |
| last_indexed | 2024-04-10T18:48:29Z |
| publishDate | 2023-01-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Ibn Al-Haitham Journal for Pure and Applied Sciences |
| spelling | doaj.art-a91c0cf6a95c44119697ee082eaf0da02023-02-01T08:52:15ZengUniversity of BaghdadIbn Al-Haitham Journal for Pure and Applied Sciences1609-40422521-34072023-01-0136111312210.30526/36.1.28944265Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion MatrixHaider S. Kaduhm0Hameed M. Abduljabbar1Department of Phesics,College of Education for Pure Sciences, Ibn Al - Haitham, University of Baghdad, Baghdad, Iraq.Department of Phesics,College of Education for Pure Sciences, Ibn Al - Haitham, University of Baghdad, Baghdad, Iraq.In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between every Two cases or two steps (two different angles and for the same number of classes). The agreement percentage between the classification results and the various methods was calculated.https://jih.uobaghdad.edu.iq/index.php/j/article/view/2894keywords: k-means, feature extraction, confusion matrix, agreement percent, class projection |
| spellingShingle | Haider S. Kaduhm Hameed M. Abduljabbar Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix Ibn Al-Haitham Journal for Pure and Applied Sciences keywords: k-means, feature extraction, confusion matrix, agreement percent, class projection |
| title | Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix |
| title_full | Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix |
| title_fullStr | Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix |
| title_full_unstemmed | Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix |
| title_short | Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix |
| title_sort | studying the classification of texture images by k means of co occurrence matrix and confusion matrix |
| topic | keywords: k-means, feature extraction, confusion matrix, agreement percent, class projection |
| url | https://jih.uobaghdad.edu.iq/index.php/j/article/view/2894 |
| work_keys_str_mv | AT haiderskaduhm studyingtheclassificationoftextureimagesbykmeansofcooccurrencematrixandconfusionmatrix AT hameedmabduljabbar studyingtheclassificationoftextureimagesbykmeansofcooccurrencematrixandconfusionmatrix |