Adaptive low-rank multi-label active learning for image classification

Jian Wu, Anqian Guo, Victor S. Sheng, Pengpeng Zhao, Zhiming Cui, Hua Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Multi-label active learning for image classification has attracted great attention over recent years and a lot of relevant works are published continuously. However, there still remain some problems that need to be solved, such as existing multi-label active learning algorithms do not reflect on the cleanness of sample data and their ways on label correlation mining are defective. For one thing, sample data is usually contaminated in reality, which disturbs the estimation of data distribution and further hinders the model training. For another, previous approaches for label relationship exploration are purely based on the observed label distribution of an incomplete training set, which cannot provide sufficiently efficient information. To address these issues, we propose a novel adaptive low-rank multi-label active learning algorithm, called LRMAL. Specifically, we first use low-rank matrix recovery to learn an effective low-rank feature representation from the noisy data. In a subsequent sampling phase, we make use of its superiorities to evaluate the general informativeness of each unlabelled example-label pair. Based on an intrinsic mapping relation between the example space and the label space of a certain multi-label dataset, we recover the incomplete labels of a training set for a more comprehensive label correlation mining. Furthermore, to reduce the redundancy among the selected example-label pairs, we use a diversity measurement to diversify the sampled data. Finally, an effective sampling strategy is developed by integrating these two aspects of potential information with uncertainty based on an adaptive integration scheme. Experimental results demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationMM 2017 - Proceedings of the 2017 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages1336-1344
Number of pages9
ISBN (Electronic)9781450349062
DOIs
StatePublished - Oct 23 2017
Event25th ACM International Conference on Multimedia, MM 2017 - Mountain View, United States
Duration: Oct 23 2017Oct 27 2017

Publication series

NameMM 2017 - Proceedings of the 2017 ACM Multimedia Conference

Conference

Conference25th ACM International Conference on Multimedia, MM 2017
CountryUnited States
CityMountain View
Period10/23/1710/27/17

Keywords

  • Active learning
  • Label correlation
  • Low-rank representation
  • Multi-label image classification

Fingerprint Dive into the research topics of 'Adaptive low-rank multi-label active learning for image classification'. Together they form a unique fingerprint.

  • Cite this

    Wu, J., Guo, A., Sheng, V. S., Zhao, P., Cui, Z., & Li, H. (2017). Adaptive low-rank multi-label active learning for image classification. In MM 2017 - Proceedings of the 2017 ACM Multimedia Conference (pp. 1336-1344). (MM 2017 - Proceedings of the 2017 ACM Multimedia Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3123266.3123388