Robust characteristics for texture classification
| dc.contributor.author | Maarouf, Abderrazak Ayoub | |
| dc.contributor.author | Hachouf, Fella | |
| dc.date.accessioned | 2024-03-12T18:37:27Z | |
| dc.date.available | 2024-03-12T18:37:27Z | |
| dc.date.issued | 2021-05-25 | |
| dc.description.abstract | In this paper, an exhaustive search for relevant characteristics for automatic texture classification has been carried out. These features have been extracted from different cooperative methods dealing with texture characterization. An optimal features vector has been constructed using genetic algorithms (GA) to avoid characteristics redundancy . Then texture classification has been performed using multi-class SVM, k-nearest neighbors, and random forest classifier algorithms. Obtained results on three texture databases are very satisfying against those produced by existing methods. | |
| dc.identifier.isbn | 978-9931-9788-0-0 | |
| dc.identifier.uri | http://dspace.univ-oeb.dz:4000/handle/123456789/18733 | |
| dc.language.iso | en | |
| dc.publisher | University of Oum El Bouaghi | |
| dc.subject | Texture; classification; genetic algorithms; optimal features; machine learning. | |
| dc.title | Robust characteristics for texture classification | |
| dc.type | Article |
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