Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise.
Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise.
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DOI：
10.1145/3379504
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发表时间：
2020-06
影响因子：
16.6
通讯作者：
Zhao P
中科院分区：
文献类型：
--
作者：
Wu J;Sheng VS;Zhang J;Li H;Dadakova T;Swisher CL;Cui Z;Zhao P
图像分类是图像理解中的一个关键问题，多标签图像分类是近年来的研究热点。然而，多标签图像分类的成功与否与训练集的构造方式密切相关。由于主动学习的目的是通过迭代地选择信息量最大的样本来构造有效的训练集，从而从标注器中查询标签，因此将其引入多标签图像分类。因此，多标签主动学习成为一个重要的研究方向。在这项工作中，我们首先回顾现有的多标签主动学习算法的图像分类。这些算法可以分为两大类，分别从两个方面：采样和注释。多标签主动学习最重要的组成部分是设计一个有效的抽样策略，根据各种信息措施，从未标记的数据池中主动选择具有最高信息含量的示例。因此，在这次调查中强调了不同的信息性措施。此外，本文还对多标签主动学习中存在的挑战性问题和未来的发展前景进行了深入研究，重点关注四个核心方面：示例维度，标签维度，注释和应用扩展。
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.