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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| Veröffentlicht in: | IEEE transactions on image processing Jg. 28; H. 11; S. 5716 - 5728 |
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| Sprache: | Englisch |
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United States
IEEE
01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
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| Abstract | 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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| AbstractList | In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce selection parameter for each image/video with weak labels, and expect 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 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. Experimental results validate the effectiveness of our method. 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. 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.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. |
| Author | Yan Zhou Liantao Wang Qingwu Li |
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| SubjectTerms | 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 |
| Title | Multiple-Instance Discriminant Analysis for Weakly Supervised Segment Annotation |
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