TY - JOUR
T1 - Multi-Label Active Learning Algorithms for Image Classification
T2 - Overview and Future Promise
AU - Wu, Jian
AU - Sheng, Victor S.
AU - Zhang, Jing
AU - Li, Hua
AU - Dadakova, Tetiana
AU - Swisher, Christine Leon
AU - Cui, Zhiming
AU - Zhao, Pengpeng
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
KW - Image classification
KW - active learning
KW - annotation
KW - multi-label image
KW - sampling strategy
UR - http://www.scopus.com/inward/record.url?scp=85087869877&partnerID=8YFLogxK
U2 - 10.1145/3379504
DO - 10.1145/3379504
M3 - Review article
AN - SCOPUS:85087869877
SN - 0360-0300
VL - 53
JO - ACM Computing Surveys
JF - ACM Computing Surveys
IS - 2
M1 - 3379504
ER -