Optimizing Edge Detection for Image Segmentation with Multicut Penalties Article Swipe
Steffen Jung
,
Sebastian Ziegler
,
Amirhossein Kardoost
,
Margret Keuper
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.05416
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.05416
The Minimum Cost Multicut Problem (MP) is a popular way for obtaining a graph decomposition by optimizing binary edge labels over edge costs. While the formulation of a MP from independently estimated costs per edge is highly flexible and intuitive, solving the MP is NP-hard and time-expensive. As a remedy, recent work proposed to predict edge probabilities with awareness to potential conflicts by incorporating cycle constraints in the prediction process. We argue that such formulation, while providing a first step towards end-to-end learnable edge weights, is suboptimal, since it is built upon a loose relaxation of the MP. We therefore propose an adaptive CRF that allows to progressively consider more violated constraints and, in consequence, to issue solutions with higher validity. Experiments on the BSDS500 benchmark for natural image segmentation as well as on electron microscopic recordings show that our approach yields more precise edge detection and image segmentation.
Related Topics
Concepts
Enhanced Data Rates for GSM Evolution
Benchmark (surveying)
Segmentation
Computer science
Image (mathematics)
Image segmentation
Relaxation (psychology)
Process (computing)
Artificial intelligence
Edge detection
Mathematical optimization
Algorithm
Mathematics
Image processing
Geodesy
Social psychology
Geography
Operating system
Psychology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2112.05416
- https://arxiv.org/pdf/2112.05416
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200635807
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4200635807Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2112.05416Digital Object Identifier
- Title
-
Optimizing Edge Detection for Image Segmentation with Multicut PenaltiesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-10Full publication date if available
- Authors
-
Steffen Jung, Sebastian Ziegler, Amirhossein Kardoost, Margret KeuperList of authors in order
- Landing page
-
https://arxiv.org/abs/2112.05416Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2112.05416Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2112.05416Direct OA link when available
- Concepts
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Enhanced Data Rates for GSM Evolution, Benchmark (surveying), Segmentation, Computer science, Image (mathematics), Image segmentation, Relaxation (psychology), Process (computing), Artificial intelligence, Edge detection, Mathematical optimization, Algorithm, Mathematics, Image processing, Geodesy, Social psychology, Geography, Operating system, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.detection | 145 |
| abstract_inverted_index.estimated | 31 |
| abstract_inverted_index.learnable | 82 |
| abstract_inverted_index.obtaining | 11 |
| abstract_inverted_index.potential | 60 |
| abstract_inverted_index.providing | 76 |
| abstract_inverted_index.solutions | 117 |
| abstract_inverted_index.therefore | 99 |
| abstract_inverted_index.validity. | 120 |
| abstract_inverted_index.end-to-end | 81 |
| abstract_inverted_index.intuitive, | 39 |
| abstract_inverted_index.optimizing | 16 |
| abstract_inverted_index.prediction | 68 |
| abstract_inverted_index.recordings | 136 |
| abstract_inverted_index.relaxation | 94 |
| abstract_inverted_index.Experiments | 121 |
| abstract_inverted_index.constraints | 65, 111 |
| abstract_inverted_index.formulation | 25 |
| abstract_inverted_index.microscopic | 135 |
| abstract_inverted_index.suboptimal, | 86 |
| abstract_inverted_index.consequence, | 114 |
| abstract_inverted_index.formulation, | 74 |
| abstract_inverted_index.segmentation | 129 |
| abstract_inverted_index.decomposition | 14 |
| abstract_inverted_index.incorporating | 63 |
| abstract_inverted_index.independently | 30 |
| abstract_inverted_index.probabilities | 56 |
| abstract_inverted_index.progressively | 107 |
| abstract_inverted_index.segmentation. | 148 |
| abstract_inverted_index.time-expensive. | 46 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |