Improved dominant local binary pattern texture features
Autor: | Shahera Hossain, Niraj P. Doshi, Gerald Schaefer |
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Rok vydání: | 2016 |
Předmět: |
Pixel
Computer science Local binary patterns business.industry Texture Descriptor ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology 01 natural sciences Texture (geology) Image (mathematics) 010309 optics Support vector machine Set (abstract data type) Histogram 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
Zdroj: | 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV). |
DOI: | 10.1109/iciev.2016.7760181 |
Popis: | Texture features are important in many computer vision applications. LBP is a simple yet powerful texture descriptor that is based on the concept of local binary patterns which describe the relationships of pixels to their local neighbourhood. These relationships are encoded in binary form, and the resulting patterns are then typically used to build histograms over an image or image region. It is observed that only relatively few of these patterns occur frequently in images. Dominant LBP (D-LBP) is a variant of LBP based on these dominant LBP patterns. D-LBP re-arranges the histogram bins in descending order of frequency and then selects the first few dominant patterns as texture features. By doing so, however, it discards the information of which patterns are selected. In this paper, we propose an improved Dominant LBP algorithm that preserves the pattern information and show it, based on an extensive set of experiments on several Outex benchmark datasets, to outperform D-LBP for texture classification. |
Databáze: | OpenAIRE |
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