A discriminative and sparse topic model for image classification and annotation

作者：

Highlights：

• The label information is enforced in the generation of visual and annotation terms.

• The zero-mean Laplace distribution is added to a topic generative process.

• The sparse image representation is helpful to learn a training model.

• A series of experiments on four data sets demonstrate the performance of DSTM.

摘要

•The label information is enforced in the generation of visual and annotation terms.•The zero-mean Laplace distribution is added to a topic generative process.•The sparse image representation is helpful to learn a training model.•A series of experiments on four data sets demonstrate the performance of DSTM.

论文关键词：Graphical model,Discriminative topic,Sparsity,Image classification,Image annotation

论文评审过程：Received 14 June 2014, Revised 22 August 2015, Accepted 16 March 2016, Available online 5 April 2016, Version of Record 24 April 2016.

论文官网地址：https://doi.org/10.1016/j.imavis.2016.03.005