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图像多标签学习的研究概述
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国家自然科学基金（61572503，61872424，6193000388）；南京邮电大学高层次人才启动基金（NY218001）；模式识别国家重点实验室开放课题（201900015）


A survey of image multi-label learning
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    摘要:

    随着图像大数据的爆发，特别是用户贡献数据的飞速增长，图像样本的语义内容越来越丰富，标签信息也随之越来越复杂.因此图像多标签学习的研究是近年来学术圈和产业界的研究热点之一，涌现了大量表现优异的方法和技术.基于此，本文将对近年来图像多标签学习上的研究成果进行总结.首先，对多标签学习进行简单介绍，并详述其主流方法的分类；随后，针对目前大数据时代的数据特性，总结了多标签学习面临的新的技术难点及其对应的解决方案；最后，在应用层面上介绍了多标签学习在医学、计算机科学等领域的应用实例.

    Abstract:

    With the fast growing number of images,especially the user-generated ones,the semantic content of images become richer,and labels become more complex.Therefore,the study on image multi-label learning is one of the hot research areas in both academia and industry,and a large number of efficient methods have emerged in recent years.This paper surveys the existing work on image multi-label learning in recent years.Firstly,we briefly describe the concept of multi-label learning and introduce two types of methods,that is,single-instance multi-label learning and multi-instance multi-label learning.Then,we summarize three challenges on multi-label learning caused by the big data characteristics,and provide related work which can handle these challenges.Finally,we elaborate two applications on image recognition and automatic drive to show that multi-label learning techniques can be effective for many application scenarios.

    参考文献
    [1] Zhou Z H,Zhang M L,Huang S J,et al.Multi-instance multi-label learning[J].Artificial Intelligence,2012,176(1):2291-2320
    [2] Tsoumakas G,Katakis I.Multi-label classification[J].International Journal of Data Warehousing and Mining,2007,3(3):1-13
    [3] Read J,Pfahringer B,Holmes G,et al.Classifier chains for multi-label classification[J].Machine Learning,2011,85(3):333-359
    [4] Nam J,Kim J,Gurevych I,et al.Large-scale multi-label text classification:revisiting neural networks[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases,2013:437-452
    [5] Chen S F,Chen Y C,Yeh C K,et al.Order-free RNN with visual attention for multi-label classification[J].Proceedings of the AAAI Conference on Artificial Intelligence,2018,32:6714-6721
    [6] Markatopoulou F,Mezaris V,Patras I.Implicit and explicit concept relations in deep neural networks for multi-label video/image annotation[J].IEEE Transactions on Circuits and Systems for Video Technology,2019,29(6):1631-1644
    [7] Zhang J J,Wu Q,Shen C H,et al.Multilabel image classification with regional latent semantic dependencies[J].IEEE Transactions on Multimedia,2018,20(10):2801-2813
    [8] Li C H,Kang Q,Ge G J,et al.DeepBE:learning deep binary encoding for multi-label classification[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),2016:744-751
    [9] He S Y,Xu C,Guo T Y,et al.Reinforced multi-label image classification by exploring curriculum[J].Proceedings of the AAAI Conference on Artificial Intelligence,2018,32:3183-3190
    [10] Li Y C,Song Y L,Luo J B.Improving pairwise ranking for multi-label image classification[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2017:1837-1845
    [11] Behpour S,Xing W,Ziebart B D.ARC:adversarial robust cuts for semi-supervised and multi-label classification[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),2018:1905-1907
    [12] Yang H,Zhou J T,Zhang Y,et al.Exploit bounding box annotations for multi-label object recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016:280-288
    [13] Ding X M,Li B,Xiong W H,et al.Multi-instance multi-label learning combining hierarchical context and its application to image annotation[J].IEEE Transactions on Multimedia,2016,18(8):1616-1627
    [14] Yang H,Zhou J T Y,Cai J F,et al.MIML-FCN+:multi-instance multi-label learning via fully convolutional networks with privileged information[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2017:5996-6004
    [15] Zhu Y,Ting K M,Zhou Z H.Discover multiple novel labels in multi-instance multi-label learning[J].Proceedings of the AAAI Conference on Artificial Intelligence,2017,31:2977-2984
    [16] Wang Z X,Chen T S,Li G B,et al.Multi-label image recognition by recurrently discovering attentional regions[J].IEEE International Conference on Computer Vision (ICCV),2017:464-472
    [17] Xie M K,Huang S J.Partial multi-label learning[J].Proceedings of the AAAI Conference on Artificial Intelligence,2018,32:4302-4309
    [18] Yang H,Zhou J T,Cai J F.Improving multi-label learning with missing labels by structured semantic correlations[M]//Computer Vision-ECCV 2016.Cham:Springer International Publishing,2016:835-851.DOI:10.1007/978-3-319-46448-0_50
    [19] Sun L J,Feng S H,Wang T,et al.Partial multi-label learning by low-rank and sparse decomposition[J].Proceedings of the AAAI Conference on Artificial Intelligence,2019,33:5016-5023
    [20] Fang J P,Zhang M L.Partial multi-label learning via credible label elicitation[J].Proceedings of the AAAI Conference on Artificial Intelligence,2019,33:3518-3525
    [21] Chen X,Liew J H,Xiong W,et al.Focus,segment and erase:an efficient network for multi-label brain tumor segmentation[M]//Computer Vision-ECCV 2018.Cham:Springer International Publishing,2018:674-689.DOI:10.1007/978-3-030-01261-8_40
    [22] Wu B Y,Lyu S W,Ghanem B.Constrained submodular minimization for missing labels and class imbalance in multi-label learning[J].Proceedings of the AAAI Conference on Artificial Intelligence,2016,30:2229-2236
    [23] Huang C Q,Yang S M,Pan Y,et al.Object-location-aware hashing for multi-label image retrieval via automatic mask learning[J].IEEE Transactions on Image Processing,2018,27(9):4490-4502
    [24] Bao B K,Ni B B,Mu Y D,et al.Efficient region-aware large graph construction towards scalable multi-label propagation[J].Pattern Recognition,2011,44(3):598-606
    [25] Gupta V,Wadbude R,Natarajan N,et al.Distributional semantics meets multi-label learning[J].Proceedings of the AAAI Conference on Artificial Intelligence,2019,33:3747-3754
    [26] Hu M Y,Han H,Shan S G,et al.Multi-label learning from noisy labels with non-linear feature transformation[C]//Asian Conference on Computer Vision,2019:404-419
    [27] Bengio S,Weston J,Grangier D.Label embedding trees for large multi-class tasks[C]//NIPS'10 Proceedings of the 23rd International Conference on Neural Information Processing Systems,2010:163-171
    [28] Dembczynski K,Cheng W,Hullermeier E.Bayes optimal multi-label classification via probabilistic classifier chains[C]//International Conference on Machine Learning (ICML),2010:279-286
    [29] TsoumakasG,Katakis I,Vlahavas I.Random k-labelsets for multilabel classification[J].IEEE Transactions on Knowledge and Data Engineering,2011,23(7):1079-1089
    [30] Chen Y N,Lin H T.Feature-aware label space dimension reduction for multi-label classification[J].Advances in Neural Information Processing Systems,2012,2:1529-1537
    [31] Li X,Zhao F P,Guo Y H.Conditional restricted boltzmann machines for multi-label learning with incomplete labels[C]//Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics,2015:635-643
    [32] Liu C,Zhao P,Huang S J,et al.Dual set multi-label learning[J].Proceedings of the AAAI Conference on Artificial Intelligence,2018,32:3635-3642
    [33] Feng L,An B,He S.Collaboration based multi-label learning[J].Proceedings of the AAAI Conference on Artificial Intelligence,2019,33:3550-3557
    [34] Li Q,Qiao M Y,Bian W,et al.Conditional graphical lasso for multi-label image classification[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016:2977-2986
    [35] Jing X Y,Wu F,Li Z Q,et al.Multi-label dictionary learning for image annotation[J].IEEE Transactions on Image Processing,2016,25(6):2712-2725
    [36] Jing L P,Shen C Y,Yang L,et al.Multi-label classification by semi-supervised singular value decomposition[J].IEEE Transactions on Image Processing,2017,26(10):4612-4625
    [37] Mamani G E H,Setio A A A,Ginneken B V,et al.Organ detection in thorax abdomen CT using multi-label convolutional neural networks[C]//SPIE Medical Imaging,2017:1013416
    [38] Chen L,Zhan W J,Tian W,et al.Deep integration:a multi-label architecture for road scene recognition[J].IEEE Transactions on Image Processing,2019,28(10):4883-4898
    [39] Sirirattanapol C,Nagai M,Witayangkurn A,et al.Bangkok CCTV image through a road environment extraction system using multi-label convolutional neural network classification[J].ISPRS International Journal of Geo-Information,2019,8(3):128
    [40] Dai L Q,Yuan M K,Li Z C,et al.Hardware-efficient guided image filtering for multi-label problem[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2017:4905-4913
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袁梦奇,鲍秉坤.图像多标签学习的研究概述[J].南京信息工程大学学报(自然科学版),2019,11(6):682-689
YUAN Mengqi, BAO Bingkun. A survey of image multi-label learning[J]. Journal of Nanjing University of Information Science & Technology, 2019,11(6):682-689

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  • 收稿日期:2019-10-15
  • 在线发布日期: 2020-01-19

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