MULTI-VIEW MULTI-LABEL ACTIVE LEARNING FOR IMAGE CLASSIFICATION

被引：0
|
作者：
Zhang, Xiaoyu [1 ]
Cheng, Jian [1 ]
Xu, Changsheng [1 ]
Lu, Hanqing [1 ]
Ma, Songde [1 ]
机构：
[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
来源：
ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3 | 2009年
关键词：
Active learning; Multi-view learning; Image classification; Multi-label classification; Multi-view fusion;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Image classification is an important topic in multimedia analysis, among which multi-label image classification is a very challenging task with respect to the large demand for human annotation of multi-label samples. In this paper, we propose a multi-view multi-label active learning strategy, which integrates the mechanism of active learning and multi-view learning. On one hand we explore the sample and label uncertainties within each view; on the other hand we capture the uncertainty over different views based on multi-view fusion. Then the overall uncertainty along the sample, label and view dimensions are obtained to detect the most informative sample-label pairs. Experimental results demonstrate the effectiveness of the proposed scheme.
引用
收藏
页码：258 / 261
页数：4
相关论文
共 50 条
  • [21] Latent Semantic Aware Multi-View Multi-Label Classification
    Zhang, Changqing
    Yu, Ziwei
    Hu, Qinghua
    Zhu, Pengfei
    Liu, Xinwang
    Wang, Xiaobo
    [J]. THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 4414 - 4421
  • [22] Tensor based Multi-View Label Enhancement for Multi-Label Learning
    Zhang, Fangwen
    Jia, Xiuyi
    Li, Weiwei
    [J]. PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2020, : 2369 - 2375
  • [23] Multi-view based multi-label propagation for image annotation
    He, Zhanying
    Chen, Chun
    Bu, Jiajun
    Li, Ping
    Cai, Deng
    [J]. NEUROCOMPUTING, 2015, 168 : 853 - 860
  • [24] Multi-view multi-label learning with view feature attention allocation
    Cheng, Yusheng
    Li, Qingyan
    Wang, Yibin
    Zheng, Weijie
    [J]. NEUROCOMPUTING, 2022, 501 : 857 - 874
  • [25] Active learning with label correlation exploration for multi-label image classification
    Wu, Jian
    Ye, Chen
    Sheng, Victor S.
    Zhang, Jing
    Zhao, Pengpeng
    Cui, Zhiming
    [J]. IET COMPUTER VISION, 2017, 11 (07) : 577 - 584
  • [26] On active learning in multi-label classification
    Brinker, K
    [J]. FROM DATA AND INFORMATION ANALYSIS TO KNOWLEDGE ENGINEERING, 2006, : 206 - 213
  • [27] Multi-view Multi-label Learning with Shared Features Inconsistency
    Li, Qingyan
    Cheng, Yusheng
    [J]. NEURAL PROCESSING LETTERS, 2024, 56 (03)
  • [28] Multi-view Multi-label Learning with Incomplete Views and Labels
    Changming Zhu
    Lin Ma
    [J]. SN Computer Science, 2022, 3 (1)
  • [29] Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels
    Zhi-Fen He
    Chun-Hua Zhang
    Bin Liu
    Bo Li
    [J]. Applied Intelligence, 2023, 53 : 9444 - 9462
  • [30] Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels
    He, Zhi-Fen
    Zhang, Chun-Hua
    Liu, Bin
    Li, Bo
    [J]. APPLIED INTELLIGENCE, 2023, 53 (08) : 9444 - 9462
12345