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 |
| Published: |
Taylor & Francis Group
2021-07-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2020.1862059 |
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| Summary: | 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. |
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| ISSN: | 0954-0091 1360-0494 |