Conditional random fields meet deep neural networks for semantic segmentation: combining probabilistic graphical models with deep learning for structured prediction
Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solve...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
9 January 2018
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| In: |
IEEE signal processing magazine
Year: 2018, Jahrgang: 35, Heft: 1, Pages: 37-52 |
| ISSN: | 1558-0792 |
| DOI: | 10.1109/MSP.2017.2762355 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/MSP.2017.2762355 Verlag, lizenzpflichtig, Volltext: https://ieeexplore.ieee.org/document/8254255 
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| Verfasserangaben: | Anurag Arnab, Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Måns Larsson, Alexander Kirillov, Bogdan Savchynskyy, Carsten Rother, Fredrik Kahl, and Philip H.S. Torr |
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| 245 | 1 | 0 | |a Conditional random fields meet deep neural networks for semantic segmentation |b combining probabilistic graphical models with deep learning for structured prediction |c Anurag Arnab, Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Måns Larsson, Alexander Kirillov, Bogdan Savchynskyy, Carsten Rother, Fredrik Kahl, and Philip H.S. Torr |
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| 520 | |a Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, deep neural networks (DNNs) recently have been shown to excel at a wide range of computer vision problems due to their ability to automatically learn rich feature representations from data, as opposed to traditional handcrafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modeling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarize future research directions. | ||
| 650 | 4 | |a Computational modeling | |
| 650 | 4 | |a Computer vision | |
| 650 | 4 | |a conditional random fields | |
| 650 | 4 | |a CRF | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a deep neural networks | |
| 650 | 4 | |a DNN | |
| 650 | 4 | |a Feature extraction | |
| 650 | 4 | |a image segmentation | |
| 650 | 4 | |a Image segmentation | |
| 650 | 4 | |a learning (artificial intelligence) | |
| 650 | 4 | |a neural nets | |
| 650 | 4 | |a pixel labeling | |
| 650 | 4 | |a pixels | |
| 650 | 4 | |a probabilistic graphical models | |
| 650 | 4 | |a semantic segmentation | |
| 650 | 4 | |a Semantics | |
| 650 | 4 | |a statistical analysis | |
| 650 | 4 | |a structured prediction | |
| 650 | 4 | |a Visualization | |
| 700 | 1 | |a Zheng, Shuai |e VerfasserIn |4 aut | |
| 700 | 1 | |a Jayasumana, Sadeep |e VerfasserIn |4 aut | |
| 700 | 1 | |a Romera-Paredes, Bernardino |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Kahl, Fredrik |e VerfasserIn |4 aut | |
| 700 | 1 | |a Torr, Philip H.S. |e VerfasserIn |4 aut | |
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