Optimized texture classification by using hierarchical complex network measurements
| Autor: | L. da F. Costa, T. Chalumeau, Olivier Laligant, Fabrice Meriaudeau |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Pixel
business.industry Node (networking) Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image processing Complex network Fractal analysis Fractal Computer Science::Computer Vision and Pattern Recognition Entropy (information theory) Computer vision Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS Mathematics |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.655592 |
| Popis: | Texture characterization and classification remains an important issue in image processing and analysis. Much attention has been focused on methods involving spectral analysis and co-occurrence matrix, as well as more modern approaches such as those involving fractal dimension, entropy and criteria based in multiresolution. The present work addresses the problem of texture characterization in terms of complex networks: image pixels are represented as nodes and similarities between such pixels are mapped as links between the network nodes. It is verified that several types of textures present node degree distributions which are far distinct from those observed for random networks, suggesting complex organization of those textures. Traditional measurements of the network connectivity, including their respective hierarchical extensions, are then applied in order to obtain feature vectors from which the textures can be characterized and classified. The performance of such an approach is compared to co-occurrence methods, suggesting promising complementary perspectives. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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