Comparison between Bayesian network classifiers and SVMs for semantic localization

作者：

Highlights：

• We propose a method for the use of image descriptors as input in graphical model.

• We compare the results obtained in two datasets when using SVMs and BNCs.

• A large number of inputs decreases the results of BNCs with structural learning.

• Graphical models provide capabilities of an easier interpretation than SVMs.

• BNCs allow incremental modelling, so new information can be added to enrich them.

摘要

•We propose a method for the use of image descriptors as input in graphical model.•We compare the results obtained in two datasets when using SVMs and BNCs.•A large number of inputs decreases the results of BNCs with structural learning.•Graphical models provide capabilities of an easier interpretation than SVMs.•BNCs allow incremental modelling, so new information can be added to enrich them.

论文关键词：Semantic localization,Bayesian networks,Robotics,Robot vision,Structural learning,Indoor scene classification

论文评审过程：Received 2 April 2016, Revised 23 June 2016, Accepted 4 August 2016, Available online 4 August 2016, Version of Record 9 August 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.08.029