Multiple-Instance Discriminant Analysis for Weakly Supervised Segment Annotation

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...

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Vydané v:	IEEE transactions on image processing Ročník 28; číslo 11; s. 5716 - 5728
Hlavní autori:	Wang, Liantao, Li, Qingwu, Zhou, Yan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Annotations Clutter Discriminant analysis Eigenvalues Image segmentation linear discriminant analysis Linear programming Machine learning Multiple-instance learning Optimization Parameters Proposals Quadratic programming region selection Regularization Semantics Support vector machines Training Transformations Visualization weakly supervised segment annotation
ISSN:	1057-7149, 1941-0042, 1941-0042
On-line prístup:	Získať plný text
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