Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification?
Human protein subcellular location prediction can provide critical knowledge for understanding a protein’s function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investig...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/429049 |
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| author | Fan Yang Ying-Ying Xu Hong-Bin Shen |
| author_facet | Fan Yang Ying-Ying Xu Hong-Bin Shen |
| author_sort | Fan Yang |
| collection | DOAJ |
| description | Human protein subcellular location prediction can provide critical knowledge for understanding a protein’s function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification. |
| format | Article |
| id | doaj-art-ed90eb050a1e4181bf95da6a5e27f978 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ed90eb050a1e4181bf95da6a5e27f9782025-02-03T05:48:06ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/429049429049Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification?Fan Yang0Ying-Ying Xu1Hong-Bin Shen2Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, ChinaInstitute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, ChinaInstitute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, ChinaHuman protein subcellular location prediction can provide critical knowledge for understanding a protein’s function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.http://dx.doi.org/10.1155/2014/429049 |
| spellingShingle | Fan Yang Ying-Ying Xu Hong-Bin Shen Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification? The Scientific World Journal |
| title | Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification? |
| title_full | Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification? |
| title_fullStr | Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification? |
| title_full_unstemmed | Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification? |
| title_short | Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification? |
| title_sort | many local pattern texture features which is better for image based multilabel human protein subcellular localization classification |
| url | http://dx.doi.org/10.1155/2014/429049 |
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