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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3054464
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Jerripothula, Koteswar Rao
|e verfasserin
|4 aut
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| 245 |
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|a Image Co-Skeletonization via Co-Segmentation
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|c 2021
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|a Text
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|a ƒaComputermedien
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|a Date Completed 17.02.2021
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|a Date Revised 17.02.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Recent advances in the joint processing of a set of images have shown its advantages over individual processing. Unlike the existing works geared towards co-segmentation or co-localization, in this article, we explore a new joint processing topic: image co-skeletonization, which is defined as joint skeleton extraction of the foreground objects in an image collection. It is well known that object skeletonization in a single natural image is challenging, because there is hardly any prior knowledge available about the object present in the image. Therefore, we resort to the idea of image co-skeletonization, hoping that the commonness prior that exists across the semantically similar images can be leveraged to have such knowledge, similar to other joint processing problems such as co-segmentation. Moreover, earlier research has found that augmenting a skeletonization process with the object's shape information is highly beneficial in capturing the image context. Having made these two observations, we propose a coupled framework for co-skeletonization and co-segmentation tasks to facilitate shape information discovery for our co-skeletonization process through the co-segmentation process. While image co-skeletonization is our primary goal, the co-segmentation process might also benefit, in turn, from exploiting skeleton outputs of the co-skeletonization process as central object seeds through such a coupled framework. As a result, both can benefit from each other synergistically. For evaluating image co-skeletonization results, we also construct a novel benchmark dataset by annotating nearly 1.8 K images and dividing them into 38 semantic categories. Although the proposed idea is essentially a weakly supervised method, it can also be employed in supervised and unsupervised scenarios. Extensive experiments demonstrate that the proposed method achieves promising results in all three scenarios
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|a Journal Article
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1 |
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|a Cai, Jianfei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Jiangbo
|e verfasserin
|4 aut
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| 700 |
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|a Yuan, Junsong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 01., Seite 2784-2797
|w (DE-627)NLM09821456X
|x 1941-0042
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| 773 |
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|g volume:30
|g year:2021
|g day:01
|g pages:2784-2797
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|u http://dx.doi.org/10.1109/TIP.2021.3054464
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