Learning LBP structure by maximizing the conditional mutual information
作者:
Highlights:
• We propose a new approach to tackle high-dimensional LBP features.
• It discovers optimal LBP structure to generate discriminative features.
• We propose a MCMI scheme for LBP structure learning to handle pixel correlation.
• It demonstrates a superior performance to SOTA on various visual applications.
摘要
Highlights•We propose a new approach to tackle high-dimensional LBP features.•It discovers optimal LBP structure to generate discriminative features.•We propose a MCMI scheme for LBP structure learning to handle pixel correlation.•It demonstrates a superior performance to SOTA on various visual applications.
论文关键词:LBP structure learning,Scene recognition,Face recognition,Dynamic texture recognition,Maximal conditional mutual information
论文评审过程:Received 12 August 2014, Revised 16 December 2014, Accepted 3 February 2015, Available online 11 February 2015, Version of Record 17 June 2015.
论文官网地址:https://doi.org/10.1016/j.patcog.2015.02.001