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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2019.2921878
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|a eng
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| 100 |
1 |
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|a Wang, Liantao
|e verfasserin
|4 aut
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| 245 |
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|a Multiple-Instance Discriminant Analysis for Weakly Supervised Segment Annotation
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|c 2019
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 06.09.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce a selection parameter for each image/video with weak labels and expect that it can sift out object regions from the background clutter to train a better transformation vector. The selection parameter and the transformation parameter are incorporated into a single objective function and optimized in an alternate way. The optimization is an iteration between the eigenvalue decomposition and a set of quadratic programming. We also integrate a regularization term into the objective function to formulate the spatial constraint of segments, which is ignored in ordinary multiple-instance learning methods. The algorithm is able to overcome the limitations that arise when applying ordinary multiple-instance methods to the task. The experimental results validate the effectiveness of our method
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| 650 |
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|a Journal Article
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| 700 |
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|a Li, Qingwu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Yan
|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 28(2019), 11 vom: 14. Nov., Seite 5716-5728
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|x 1941-0042
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|g volume:28
|g year:2019
|g number:11
|g day:14
|g month:11
|g pages:5716-5728
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|u http://dx.doi.org/10.1109/TIP.2019.2921878
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