survey

Public Access

Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise

Authors:

Victor S. Sheng,

Tetiana Dadakova,

Christine Leon Swisher,

Pengpeng ZhaoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 2

Article No.: 28, Pages 1 - 35

https://doi.org/10.1145/3379504

Published: 20 March 2020 Publication History

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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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Xie WYuan MMa JLi YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Adaptive Pruning of Channel Spatial Dependability in Convolutional Neural NetworksProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681419(6073-6082)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681419
Luo JGuo JZhu ZDu YYe Y(2024)Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methodsJournal of Applied Remote Sensing10.1117/1.JRS.18.01451418:01Online publication date: 1-Jan-2024
https://doi.org/10.1117/1.JRS.18.014514
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Index Terms

Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Active learning

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 2

March 2021

848 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3388460

Editor:
Albert Zomaya
University of Sydney, Australia

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Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 March 2020

Accepted: 01 December 2019

Revised: 01 August 2019

Received: 01 February 2017

Published in CSUR Volume 53, Issue 2

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Zhou SXu HZheng ZChen JLi ZBu JWu JWang XZhu WEster M(2024)A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future DirectionsACM Computing Surveys10.1145/368903657:3(1-38)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3689036
Xie WYuan MMa JLi YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Adaptive Pruning of Channel Spatial Dependability in Convolutional Neural NetworksProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681419(6073-6082)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681419
Luo JGuo JZhu ZDu YYe Y(2024)Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methodsJournal of Applied Remote Sensing10.1117/1.JRS.18.01451418:01Online publication date: 1-Jan-2024
https://doi.org/10.1117/1.JRS.18.014514
Goswami DChakraborty S(2024)Active Batch Sampling for Multi-label Classification with Binary User Feedback2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00252(2522-2531)Online publication date: 3-Jan-2024
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Aláez DPrieto MVilladangos JAstrain J(2024)On Constructing Efficient UAV Aerodynamic Surrogate Models for Digital TwinsIEEE Transactions on Industrial Informatics10.1109/TII.2024.343110620:11(13181-13189)Online publication date: Nov-2024
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Li MQiu JShi W(2024)Macro-AUC-Driven Active Learning Strategy for Multi-Label Classification Enhancement2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR62202.2024.00052(280-286)Online publication date: 7-Aug-2024
https://doi.org/10.1109/MIPR62202.2024.00052
Ghozatlou ODatcu MFocsa AHeredia Conde MUllo S(2024)A Review and a Perspective of Deep Active Learning for Remote Sensing Image Analysis: Enhanced adaptation to user conjectureIEEE Geoscience and Remote Sensing Magazine10.1109/MGRS.2024.340342312:3(125-148)Online publication date: Sep-2024
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Zhang WWang YYou ZLi YCao GYang ZCui B(2024)NC-ALG: Graph-Based Active Learning Under Noisy Crowd2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00210(2681-2694)Online publication date: 13-May-2024
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Meinecke CGuéville EWrisley DJänicke S(2024)Is medieval distant viewing possible? : Extending and enriching annotation of legacy image collections using visual analyticsDigital Scholarship in the Humanities10.1093/llc/fqae02039:2(638-656)Online publication date: 24-Apr-2024
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