Texture Image Classification Based on Nonsubsampled Contourlet Transform and Local Binary Patterns
| Autor: | Chunxia Zhao, Zhengli Zhu, Yingkun Hou |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Contextual image classification
Computer Networks and Communications Local binary patterns Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Texture (music) Translation (geometry) Contourlet Support vector machine Image texture Computer Science::Computer Vision and Pattern Recognition Pattern recognition (psychology) Artificial intelligence business Software |
| Zdroj: | International Journal of Digital Content Technology and its Applications. 4:186-193 |
| ISSN: | 2233-9310 1975-9339 |
| DOI: | 10.4156/jdcta.vol4.issue9.23 |
| Popis: | This paper presents a new approach of texture image classification based on nonsubsampled contourlet transform, Local binary patterns and Support vector machines. Nonsubsampled contourlet transform and Local binary patterns are used to extract texture features of images, Support vector machines are used to classify texture images. Nonsubsampled contourlet transform has translation invariability. Local Binary Patterns has rotational and gray invariance. Support vector machines have good performance in a variety of pattern recognition problems. Experimental results demonstrate that the proposed method performs much better than some existing methods. It achieves higher classification accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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