A novel semisupervised support vector machine classifier based on active learning and context information

Fei Gao, Wenchao Lv, Yaotian Zhang, Jinping Sun, Jun Wang, Erfu Yang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
67 Downloads (Pure)


This paper proposes a novel semisupervised support vector machine classifier (Formula presented.) based on active learning (AL) and context information to solve the problem where the number of labeled samples is insufficient. Firstly, a new semisupervised learning method is designed using AL to select unlabeled samples as the semilabled samples, then the context information is exploited to further expand the selected samples and relabel them, along with the labeled samples train (Formula presented.) classifier. Next, a new query function is designed to enhance the reliability of the classification results by using the Euclidean distance between the samples. Finally, in order to enhance the robustness of the proposed algorithm, a fusion method is designed. Several experiments on change detection are performed by considering some real remote sensing images. The results show that the proposed algorithm in comparison with other algorithms can significantly improve the detection accuracy and achieve a fast convergence in addition to verify the effectiveness of the fusion method developed in this paper.

Original languageEnglish
Number of pages20
JournalMultidimensional Systems and Signal Processing
Early online date2 Apr 2016
Publication statusE-pub ahead of print - 2 Apr 2016


  • active learning
  • context information
  • remote sensing
  • semisupervised support vector machine

Cite this