A Completed Multi-Scale Local Statistics Pattern for Texture Classification
Binary pattern methods play a vital role in extracting texture features. However, most of existing methods struggle to capture comprehensive and discriminative texture information. This paper aims to propose a novel multi-statistic binary pattern to extract rotation invariance statistic features fo...
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| Format: | Article |
| Language: | English |
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Slovenian Society for Stereology and Quantitative Image Analysis
2024-11-01
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| Series: | Image Analysis and Stereology |
| Subjects: | |
| Online Access: | https://www.ias-iss.org/ojs/IAS/article/view/3037 |
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| _version_ | 1846140364729090048 |
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| author | Xiaochun Xu Bin Li Q.M. Jonathan Wu |
| author_facet | Xiaochun Xu Bin Li Q.M. Jonathan Wu |
| author_sort | Xiaochun Xu |
| collection | DOAJ |
| description |
Binary pattern methods play a vital role in extracting texture features. However, most of existing methods struggle to capture comprehensive and discriminative texture information. This paper aims to propose a novel multi-statistic binary pattern to extract rotation invariance statistic features for texture classification. First, this paper encodes the center pixel, mean, variance and range of local neighborhood by corresponding multi-scale threshold, and proposes the local center pattern, local mean pattern, local variance pattern and local range pattern. Then, based on the compact multi-pattern encoding strategy, the four sub-patterns are jointly encoded in a 4-bit binary pattern, named as multi-scale local statistics pattern. Finally, for comprehensive texture representation, the multi-scale local statistics pattern is jointly combined with local sign pattern and local magnitude pattern to generate a completed multi-scale local statistics pattern for texture classification. Extensive experiments conducted on three representative databases demonstrate that the proposed completed multi-scale local statistics pattern achieves competitive classification performance compared with other state-of-the-art approaches.
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| format | Article |
| id | doaj-art-a67b1c9191eb43929578000376c6a927 |
| institution | Kabale University |
| issn | 1580-3139 1854-5165 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Slovenian Society for Stereology and Quantitative Image Analysis |
| record_format | Article |
| series | Image Analysis and Stereology |
| spelling | doaj-art-a67b1c9191eb43929578000376c6a9272024-12-05T13:32:51ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652024-11-0143310.5566/ias.3037A Completed Multi-Scale Local Statistics Pattern for Texture ClassificationXiaochun XuBin LiQ.M. Jonathan Wu Binary pattern methods play a vital role in extracting texture features. However, most of existing methods struggle to capture comprehensive and discriminative texture information. This paper aims to propose a novel multi-statistic binary pattern to extract rotation invariance statistic features for texture classification. First, this paper encodes the center pixel, mean, variance and range of local neighborhood by corresponding multi-scale threshold, and proposes the local center pattern, local mean pattern, local variance pattern and local range pattern. Then, based on the compact multi-pattern encoding strategy, the four sub-patterns are jointly encoded in a 4-bit binary pattern, named as multi-scale local statistics pattern. Finally, for comprehensive texture representation, the multi-scale local statistics pattern is jointly combined with local sign pattern and local magnitude pattern to generate a completed multi-scale local statistics pattern for texture classification. Extensive experiments conducted on three representative databases demonstrate that the proposed completed multi-scale local statistics pattern achieves competitive classification performance compared with other state-of-the-art approaches. https://www.ias-iss.org/ojs/IAS/article/view/3037feature extractionstatistics encoding patterntexture classificationlocal binary pattern |
| spellingShingle | Xiaochun Xu Bin Li Q.M. Jonathan Wu A Completed Multi-Scale Local Statistics Pattern for Texture Classification Image Analysis and Stereology feature extraction statistics encoding pattern texture classification local binary pattern |
| title | A Completed Multi-Scale Local Statistics Pattern for Texture Classification |
| title_full | A Completed Multi-Scale Local Statistics Pattern for Texture Classification |
| title_fullStr | A Completed Multi-Scale Local Statistics Pattern for Texture Classification |
| title_full_unstemmed | A Completed Multi-Scale Local Statistics Pattern for Texture Classification |
| title_short | A Completed Multi-Scale Local Statistics Pattern for Texture Classification |
| title_sort | completed multi scale local statistics pattern for texture classification |
| topic | feature extraction statistics encoding pattern texture classification local binary pattern |
| url | https://www.ias-iss.org/ojs/IAS/article/view/3037 |
| work_keys_str_mv | AT xiaochunxu acompletedmultiscalelocalstatisticspatternfortextureclassification AT binli acompletedmultiscalelocalstatisticspatternfortextureclassification AT qmjonathanwu acompletedmultiscalelocalstatisticspatternfortextureclassification AT xiaochunxu completedmultiscalelocalstatisticspatternfortextureclassification AT binli completedmultiscalelocalstatisticspatternfortextureclassification AT qmjonathanwu completedmultiscalelocalstatisticspatternfortextureclassification |