Aggregating multi-scale contextual features from multiple stages for semantic image segmentation
Semantic segmentation plays a vital role in image understanding. Recent studies have attempted to achieve precise pixel-level classification by using deep networks that provide hierarchical features. These methods are trying to effectively utilise multi-level features that are extracted from the dat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-07-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2020.1862059 |
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| _version_ | 1849701291860688896 |
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| author | Dingchao Jiang Hua Qu Jihong Zhao Jianlong Zhao Meng-Yen Hsieh |
| author_facet | Dingchao Jiang Hua Qu Jihong Zhao Jianlong Zhao Meng-Yen Hsieh |
| author_sort | Dingchao Jiang |
| collection | DOAJ |
| description | Semantic segmentation plays a vital role in image understanding. Recent studies have attempted to achieve precise pixel-level classification by using deep networks that provide hierarchical features. These methods are trying to effectively utilise multi-level features that are extracted from the data and precisely reconstruct some characteristics of objects that are lost in producing high-level features. In this paper, we propose a multi-scale context U-net (MSCU-net) for semantic image segmentation. This network uses a multi-scale context block (MSCB) to aggregate multi-level features and employs the CRF layer to explicitly model the dependencies among pixels. This network significantly outperforms other state-of-the-art methods on both the PASCAL VOC 2012 and Cityscapes datasets. |
| format | Article |
| id | doaj-art-21c7d173f84141b884963f87b46e1c8c |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-21c7d173f84141b884963f87b46e1c8c2025-08-20T03:17:58ZengTaylor & Francis GroupConnection Science0954-00911360-04942021-07-0133360562210.1080/09540091.2020.18620591862059Aggregating multi-scale contextual features from multiple stages for semantic image segmentationDingchao Jiang0Hua Qu1Jihong Zhao2Jianlong Zhao3Meng-Yen Hsieh4Xi'an Jiaotong UniversityXi'an Jiaotong UniversityXi'an University of Posts and TelecommunicationsXi'an Jiaotong UniversityProvidence UniversitySemantic segmentation plays a vital role in image understanding. Recent studies have attempted to achieve precise pixel-level classification by using deep networks that provide hierarchical features. These methods are trying to effectively utilise multi-level features that are extracted from the data and precisely reconstruct some characteristics of objects that are lost in producing high-level features. In this paper, we propose a multi-scale context U-net (MSCU-net) for semantic image segmentation. This network uses a multi-scale context block (MSCB) to aggregate multi-level features and employs the CRF layer to explicitly model the dependencies among pixels. This network significantly outperforms other state-of-the-art methods on both the PASCAL VOC 2012 and Cityscapes datasets.http://dx.doi.org/10.1080/09540091.2020.1862059deep learningsemantic segmentationmulti-scale context |
| spellingShingle | Dingchao Jiang Hua Qu Jihong Zhao Jianlong Zhao Meng-Yen Hsieh Aggregating multi-scale contextual features from multiple stages for semantic image segmentation Connection Science deep learning semantic segmentation multi-scale context |
| title | Aggregating multi-scale contextual features from multiple stages for semantic image segmentation |
| title_full | Aggregating multi-scale contextual features from multiple stages for semantic image segmentation |
| title_fullStr | Aggregating multi-scale contextual features from multiple stages for semantic image segmentation |
| title_full_unstemmed | Aggregating multi-scale contextual features from multiple stages for semantic image segmentation |
| title_short | Aggregating multi-scale contextual features from multiple stages for semantic image segmentation |
| title_sort | aggregating multi scale contextual features from multiple stages for semantic image segmentation |
| topic | deep learning semantic segmentation multi-scale context |
| url | http://dx.doi.org/10.1080/09540091.2020.1862059 |
| work_keys_str_mv | AT dingchaojiang aggregatingmultiscalecontextualfeaturesfrommultiplestagesforsemanticimagesegmentation AT huaqu aggregatingmultiscalecontextualfeaturesfrommultiplestagesforsemanticimagesegmentation AT jihongzhao aggregatingmultiscalecontextualfeaturesfrommultiplestagesforsemanticimagesegmentation AT jianlongzhao aggregatingmultiscalecontextualfeaturesfrommultiplestagesforsemanticimagesegmentation AT mengyenhsieh aggregatingmultiscalecontextualfeaturesfrommultiplestagesforsemanticimagesegmentation |