Texture classification by texton: statistical versus binary.
Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3919727?pdf=render |
| _version_ | 1818316060494921728 |
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| author | Zhenhua Guo Zhongcheng Zhang Xiu Li Qin Li Jane You |
| author_facet | Zhenhua Guo Zhongcheng Zhang Xiu Li Qin Li Jane You |
| author_sort | Zhenhua Guo |
| collection | DOAJ |
| description | Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. |
| first_indexed | 2024-12-13T09:15:25Z |
| format | Article |
| id | doaj.art-014b424fcca841afba9087e9e04fe396 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T09:15:25Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-014b424fcca841afba9087e9e04fe3962022-12-21T23:52:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0192e8807310.1371/journal.pone.0088073Texture classification by texton: statistical versus binary.Zhenhua GuoZhongcheng ZhangXiu LiQin LiJane YouUsing statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor.http://europepmc.org/articles/PMC3919727?pdf=render |
| spellingShingle | Zhenhua Guo Zhongcheng Zhang Xiu Li Qin Li Jane You Texture classification by texton: statistical versus binary. PLoS ONE |
| title | Texture classification by texton: statistical versus binary. |
| title_full | Texture classification by texton: statistical versus binary. |
| title_fullStr | Texture classification by texton: statistical versus binary. |
| title_full_unstemmed | Texture classification by texton: statistical versus binary. |
| title_short | Texture classification by texton: statistical versus binary. |
| title_sort | texture classification by texton statistical versus binary |
| url | http://europepmc.org/articles/PMC3919727?pdf=render |
| work_keys_str_mv | AT zhenhuaguo textureclassificationbytextonstatisticalversusbinary AT zhongchengzhang textureclassificationbytextonstatisticalversusbinary AT xiuli textureclassificationbytextonstatisticalversusbinary AT qinli textureclassificationbytextonstatisticalversusbinary AT janeyou textureclassificationbytextonstatisticalversusbinary |