Automatic image annotation using semi-supervised generative modeling

作者：

Highlights：

• We propose a modified EM algorithm to incorporate unlabeled images in training phase.

• Grouping images using spectral clustering improves prototypes and models of concepts.

• For noisy annotated images, semi-supervised mixture model outperforms graph learning.

• Incorporating unlabeled images will improve annotation performance significantly.

摘要

•We propose a modified EM algorithm to incorporate unlabeled images in training phase.•Grouping images using spectral clustering improves prototypes and models of concepts.•For noisy annotated images, semi-supervised mixture model outperforms graph learning.•Incorporating unlabeled images will improve annotation performance significantly.

论文关键词：Image annotation,Semi-supervised learning,Generative modeling,Gamma distribution

论文评审过程：Received 30 October 2013, Revised 11 June 2014, Accepted 9 July 2014, Available online 19 July 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.07.012