CONSTRAINED SPECTRAL CLUSTERING FOR IMAGE SEGMENTATION
Constrained spectral clustering with affinity propagation in its original form is not practical for large scale problems like image segmentation. In this paper we employ novelty selection sub-sampling strategy, besides using efficient numerical eigen-decomposition methods to make this algorithm work efficiently for images. In addition, entropy-based active learning is also employed to select the queries posed to the user more wisely in an interactive image segmentation framework. We evaluate the algorithm on general and medical images to show that the segmentation results will improve using constrained clustering even if one works with a subset of pixels. Furthermore, this happens more efficiently when pixels to be labeled are selected actively.
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2012 |
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| Erschienen: |
2012 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2013 |
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| Enthalten in: |
IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing - 2013(2012) vom: 31. Dez., Seite 1-6 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sourati, Jamshid [VerfasserIn] |
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| Themen: |
Active learning |
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| Anmerkungen: |
Date Revised 21.10.2021 published: Print Citation Status PubMed-not-MEDLINE |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM234873388 |
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| 245 | 1 | 0 | |a CONSTRAINED SPECTRAL CLUSTERING FOR IMAGE SEGMENTATION |
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| 520 | |a Constrained spectral clustering with affinity propagation in its original form is not practical for large scale problems like image segmentation. In this paper we employ novelty selection sub-sampling strategy, besides using efficient numerical eigen-decomposition methods to make this algorithm work efficiently for images. In addition, entropy-based active learning is also employed to select the queries posed to the user more wisely in an interactive image segmentation framework. We evaluate the algorithm on general and medical images to show that the segmentation results will improve using constrained clustering even if one works with a subset of pixels. Furthermore, this happens more efficiently when pixels to be labeled are selected actively | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Constrained spectral clustering | |
| 650 | 4 | |a active learning | |
| 650 | 4 | |a image segmentation | |
| 700 | 1 | |a Brooks, Dana H |e verfasserin |4 aut | |
| 700 | 1 | |a Dy, Jennifer G |e verfasserin |4 aut | |
| 700 | 1 | |a Erdogmus, Deniz |e verfasserin |4 aut | |
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