Multi-resolution Laws’ Masks based texture classification
Wavelet transforms are widely used for texture feature extraction. For dyadic transform, frequency splitting is coarse and the orientation selection is even poorer. Laws’ mask is a traditional technique for extraction of texture feature whose main approach is towards filtering of images with five ty...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.localhost:article/764 |
| Acceso en línea: | https://jart.icat.unam.mx/index.php/jart/article/view/764 |
| Access Level: | acceso abierto |
| Palabra clave: | Multi-resolution Laws’ Masks Dyadic wavelet transform Feature extraction Texture classification |
| Sumario: | Wavelet transforms are widely used for texture feature extraction. For dyadic transform, frequency splitting is coarse and the orientation selection is even poorer. Laws’ mask is a traditional technique for extraction of texture feature whose main approach is towards filtering of images with five types of masks, namely level, edge, spot, ripple, and wave. With each combination of these masks, it gives discriminative information. A new approach for texture classification based on the combination of dyadic wavelet transform with different wavelet basis functions and Laws’ masks named as Multi-resolution Laws’ Masks (MRLM) is proposed in this paper to further improve the performance of Laws’ mask descriptor. A k-Nearest Neighbor (k-NN) classifier is employed to classify each texture into appropriate class. Two challenging databases Brodatz and VisTex are used for the evaluation of the proposed method. Extensive experiments show that the Multi-resolution Laws’ Masks can achieve better classificationaccuracy than existing dyadic wavelet transform and Laws’ masks methods. |
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