Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise
Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an e...
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| Published in: | ACM computing surveys Vol. 53; no. 2 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.06.2020
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| ISSN: | 0360-0300 |
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| Abstract | Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an effective training set through iteratively selecting the most informative examples to query labels from annotators, it was introduced into multi-label image classification. Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification. These algorithms can be categorized into two top groups from two aspects respectively: sampling and annotation. The most important component of multi-label active learning is to design an effective sampling strategy that actively selects the examples with the highest informativeness from an unlabeled data pool, according to various information measures. Thus, different informativeness measures are emphasized in this survey. Furthermore, this work also makes a deep investigation on existing challenging issues and future promises in multi-label active learning with a focus on four core aspects: example dimension, label dimension, annotation, and application extension. |
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| AbstractList | Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an effective training set through iteratively selecting the most informative examples to query labels from annotators, it was introduced into multi-label image classification. Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification. These algorithms can be categorized into two top groups from two aspects respectively: sampling and annotation. The most important component of multi-label active learning is to design an effective sampling strategy that actively selects the examples with the highest informativeness from an unlabeled data pool, according to various information measures. Thus, different informativeness measures are emphasized in this survey. Furthermore, this work also makes a deep investigation on existing challenging issues and future promises in multi-label active learning with a focus on four core aspects: example dimension, label dimension, annotation, and application extension. Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an effective training set through iteratively selecting the most informative examples to query labels from annotators, it was introduced into multi-label image classification. Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification. These algorithms can be categorized into two top groups from two aspects respectively: sampling and annotation. The most important component of multi-label active learning is to design an effective sampling strategy that actively selects the examples with the highest informativeness from an unlabeled data pool, according to various information measures. Thus, different informativeness measures are emphasized in this survey. Furthermore, this work also makes a deep investigation on existing challenging issues and future promises in multi-label active learning with a focus on four core aspects: example dimension, label dimension, annotation, and application extension.Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an effective training set through iteratively selecting the most informative examples to query labels from annotators, it was introduced into multi-label image classification. Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification. These algorithms can be categorized into two top groups from two aspects respectively: sampling and annotation. The most important component of multi-label active learning is to design an effective sampling strategy that actively selects the examples with the highest informativeness from an unlabeled data pool, according to various information measures. Thus, different informativeness measures are emphasized in this survey. Furthermore, this work also makes a deep investigation on existing challenging issues and future promises in multi-label active learning with a focus on four core aspects: example dimension, label dimension, annotation, and application extension. |
| Author | Zhang, Jing Li, Hua Swisher, Christine Leon Wu, Jian Sheng, Victor S Cui, Zhiming Dadakova, Tetiana Zhao, Pengpeng |
| Author_xml | – sequence: 1 givenname: Jian surname: Wu fullname: Wu, Jian organization: Soochow University, China and Human Longevity, Inc., USA – sequence: 2 givenname: Victor S surname: Sheng fullname: Sheng, Victor S organization: Texas Tech University, USA – sequence: 3 givenname: Jing surname: Zhang fullname: Zhang, Jing organization: Nanjing University of Science and Technology, China – sequence: 4 givenname: Hua surname: Li fullname: Li, Hua organization: Washington University in St. Louis, USA – sequence: 5 givenname: Tetiana surname: Dadakova fullname: Dadakova, Tetiana organization: Human Longevity, Inc., USA – sequence: 6 givenname: Christine Leon surname: Swisher fullname: Swisher, Christine Leon organization: Human Longevity, Inc., USA – sequence: 7 givenname: Zhiming surname: Cui fullname: Cui, Zhiming organization: Suzhou University of Science and Technology, China – sequence: 8 givenname: Pengpeng surname: Zhao fullname: Zhao, Pengpeng organization: Soochow University, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34421185$$D View this record in MEDLINE/PubMed |
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