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Deep Multiphase Level Set for Scene Parsing
Recently, Fully Convolutional Network (FCN) seems to be the go-to architecture for image segmentation, including semantic scene parsing. However, it is difficult for a generic FCN to predict semantic labels around the object boundaries, thus FCN-based methods usually produce parsing results with ina...
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Published in: | IEEE transactions on image processing 2020-01, Vol.29, p.4556-4567 |
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creator | Zhang, Pingping Liu, Wei Lei, Yinjie Wang, Hongyu Lu, Huchuan |
description | Recently, Fully Convolutional Network (FCN) seems to be the go-to architecture for image segmentation, including semantic scene parsing. However, it is difficult for a generic FCN to predict semantic labels around the object boundaries, thus FCN-based methods usually produce parsing results with inaccurate boundaries. Meanwhile, many works have demonstrate that level set based active contours are superior to the boundary estimation in sub-pixel accuracy. However, they are quite sensitive to initial settings. To address these limitations, in this paper we propose a novel Deep Multiphase Level Set (DMLS) method for semantic scene parsing, which efficiently incorporates multiphase level sets into deep neural networks. The proposed method consists of three modules, i.e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning. More specifically, recurrent FCNs learn multi-level representations of input images with different contexts. Adaptive multiphase level set drives the discriminative contour for each semantic class, which makes use of the advantages of both global and local information. In each time-step of the recurrent FCNs, deeply supervised learning is incorporated for model training. Extensive experiments on three public benchmarks have shown that our proposed method achieves new state-of-the-art performances. The source codes will be released at https://github.com/Pchank/DMLS-for-SSP. |
doi_str_mv | 10.1109/TIP.2019.2957915 |
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However, it is difficult for a generic FCN to predict semantic labels around the object boundaries, thus FCN-based methods usually produce parsing results with inaccurate boundaries. Meanwhile, many works have demonstrate that level set based active contours are superior to the boundary estimation in sub-pixel accuracy. However, they are quite sensitive to initial settings. To address these limitations, in this paper we propose a novel Deep Multiphase Level Set (DMLS) method for semantic scene parsing, which efficiently incorporates multiphase level sets into deep neural networks. The proposed method consists of three modules, i.e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning. More specifically, recurrent FCNs learn multi-level representations of input images with different contexts. Adaptive multiphase level set drives the discriminative contour for each semantic class, which makes use of the advantages of both global and local information. In each time-step of the recurrent FCNs, deeply supervised learning is incorporated for model training. Extensive experiments on three public benchmarks have shown that our proposed method achieves new state-of-the-art performances. The source codes will be released at https://github.com/Pchank/DMLS-for-SSP.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2019.2957915</identifier><identifier>PMID: 32086208</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Active contours ; Artificial neural networks ; Boundaries ; DSL ; Feature extraction ; Image segmentation ; Level set ; Machine learning ; Multiphase ; multiphase level set ; object boundary estimation ; Production methods ; recurrent convolutional network ; Semantic scene parsing ; Semantics ; Supervised learning</subject><ispartof>IEEE transactions on image processing, 2020-01, Vol.29, p.4556-4567</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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subjects | Active contours Artificial neural networks Boundaries DSL Feature extraction Image segmentation Level set Machine learning Multiphase multiphase level set object boundary estimation Production methods recurrent convolutional network Semantic scene parsing Semantics Supervised learning |
title | Deep Multiphase Level Set for Scene Parsing |
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