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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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 28; no. 11; pp. 5716 - 5728
Main Authors: Wang, Liantao, Li, Qingwu, Zhou, Yan
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2019.2921878