Eye tracking data guided feature selection for image classification

作者：

Highlights：

• Proposing to leverage the value of eye tracking data for image classification.

• Considering the fitness value and generation number in the rotation angle of quantum genetic algorithm.

• Sequentially implementing mRMR and SVM-RFE method and incorporating the ranking information of the mRMR selector in the ranking procedure of the SVM-RFE.

摘要

Highlights•Proposing to leverage the value of eye tracking data for image classification.•Considering the fitness value and generation number in the rotation angle of quantum genetic algorithm.•Sequentially implementing mRMR and SVM-RFE method and incorporating the ranking information of the mRMR selector in the ranking procedure of the SVM-RFE.

论文关键词：Eye tracking,Feature selection,Quantum genetic algorithm (QGA),mRMR,SVM-RFE

论文评审过程：Received 20 September 2014, Revised 9 September 2016, Accepted 10 September 2016, Available online 12 September 2016, Version of Record 29 September 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.007