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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2810541
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|a eng
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| 100 |
1 |
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|a Xiao, Xiaolin
|e verfasserin
|4 aut
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| 245 |
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|a Content-Adaptive Superpixel Segmentation
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| 264 |
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|c 2018
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|a Text
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|a ƒaComputermedien
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|a Date Completed 30.07.2018
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|a Date Revised 30.07.2018
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Superpixel segmentation targets at grouping pixels in an image into atomic regions whose boundaries align well with the natural object boundaries. This paper first proposes a new feature representation for superpixel segmentation that holistically embraces color, contour, texture, and spatial features. Then, we introduce a clustering-based discriminability measure to iteratively evaluate the importance of different features. Integrating the feature representation and the discriminability measure, we propose a novel content-adaptive superpixel (CAS) segmentation algorithm. CAS is able to automatically and iteratively adjust the weights of different features to fit various properties of image instances. Experiments on several challenging datasets demonstrate that the proposed CAS outperforms the state-of-the-art methods and has a low computational cost
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|a Journal Article
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|a Zhou, Yicong
|e verfasserin
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|a Gong, Yue-Jiao
|e verfasserin
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 6 vom: 27. Juni, Seite 2883-2896
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|g volume:27
|g year:2018
|g number:6
|g day:27
|g month:06
|g pages:2883-2896
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|u http://dx.doi.org/10.1109/TIP.2018.2810541
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