Abstract
This work proposes a new method of texture analysis for grey-level images based on the distribution of connectivity indexes in local neighbourhoods. The connectivity index acts as a measure of homogeneity of textures and its distribution is computed at various local neighbourhood sizes. The resulting descriptors provide an efficient multiscale representation of connectivity at different scales. The method was tested in the classification of UIUC, Outex, and KTH-TIPS2b databases and outperformed several state-of-the-art approaches, including such as LBP, LBP+VAR, MR8, multifractals among others.
Original language | English |
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Pages (from-to) | 239–244 |
Journal | Pattern Recognition Letters |
Volume | 84 |
Early online date | 29 Sept 2016 |
DOIs | |
Publication status | Published - Dec 2016 |
Keywords
- Local connectivity
- Texture analysis
- Pattern recognition
- Image classification