Generative regularization with latent topics for discriminative object recognition

作者：

Highlights：

• We group parts and instances to model topics of objects for the recognition problem.

• We jointly learn to classify objects as well as to perform the grouping of parts and instances into topics.

• The grouping improves performance in PASCAL VOC dataset and the MIT indoor dataset.

摘要

Highlights•We group parts and instances to model topics of objects for the recognition problem.•We jointly learn to classify objects as well as to perform the grouping of parts and instances into topics.•The grouping improves performance in PASCAL VOC dataset and the MIT indoor dataset.

论文关键词：Visual object recognition,Part-based models,Non-Negative Matrix Factorization,Latent SVM,Mixture models

论文评审过程：Received 3 January 2015, Revised 1 June 2015, Accepted 23 June 2015, Available online 2 July 2015, Version of Record 19 August 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.06.013