Quadratic divergence regularized SVM for optic disc segmentation

Jun Cheng, Dacheng Tao, Damon Wing Kee Wong, Jiang Liu

Research output: Journal PublicationArticlepeer-review

21 Citations (Scopus)


Machine learning has been used in many retinal image processing applications such as optic disc segmentation. It assumes that the training and testing data sets have the same feature distribution. However, retinal images are often collected under different conditions and may have different feature distributions. Therefore, the models trained from one data set may not work well for another data set. However, it is often too expensive and time consuming to label the needed training data and rebuild the models for all different data sets. In this paper, we propose a novel quadratic divergence regularized support vector machine (QDSVM) to transfer the knowledge from domains with sufficient training data to domains with limited or even no training data. The proposed method simultaneously minimizes the distribution difference between the source domain and target domain while training the classifier. Experimental results show that the proposed transfer learning based method reduces the classification error in superpixel level from 14.2% without transfer learning to 2.4% with transfer learning. The proposed method is effective to transfer the label knowledge from source to target domain, which enables it to be used for optic disc segmentation in data sets with different feature distributions.

Original languageEnglish
Pages (from-to)2687-2696
Number of pages10
JournalBiomedical Optics Express
Issue number5
Publication statusPublished - 1 May 2017
Externally publishedYes


  • Image analysis
  • Image processing
  • Image recognition, algorithms and filters

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics


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