Performance Comparison of Texture Classifiers on Small Windows
| dc.contributor.author | Baykal, Ibrahim Cem | |
| dc.date.accessioned | 2025-01-06T17:29:43Z | |
| dc.date.available | 2025-01-06T17:29:43Z | |
| dc.date.issued | 2019 | |
| dc.description | 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019 -- 21 September 2019 through 22 September 2019 -- Malatya -- 153040 | |
| dc.description.abstract | A textured image has both random and periodic components. Texture is defined as the statistical variance of the pixel values with respect to each other therefore, it is impossible to classify a single pixel of any texture. They must be considered as a group or simply as a window. There has to be enough pixels in that window to define the properties of that texture. The smaller the window is, the harder for the texture classifier to recognize it. On the other hand, using smaller windows reduce the processing power requirements and make it easier to localize defects or segment textures. Consequently, the aim of this article is to evaluate the performance of popular texture classifiers on different window sizes. In this article, the performance of a Support Vector Machine classifier versus an Artificial Neural Network is compared as well. Results show that GLCM-16 combined with an Artificial Neural Network is superior to GLCM-256 and LBP variants on small windows. © 2019 IEEE. | |
| dc.identifier.doi | 10.1109/IDAP.2019.8875882 | |
| dc.identifier.isbn | 978-172812932-7 | |
| dc.identifier.scopus | 2-s2.0-85074877772 | |
| dc.identifier.uri | https://doi.org/10.1109/IDAP.2019.8875882 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14669/1315 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019 | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_20241211 | |
| dc.subject | ANN | |
| dc.subject | Image Processing | |
| dc.subject | Pattern Recognition | |
| dc.subject | SVM | |
| dc.subject | Texture Classification | |
| dc.subject | Texture Feature extraction | |
| dc.title | Performance Comparison of Texture Classifiers on Small Windows | |
| dc.type | Conference Object |
