A novel texture feature based multiple classifier technique for roadside vegetation classification

作者：

Highlights：

• Proposed technique use LBP based GLCM feature vector and multiple classifiers.

• We achieve over 92% accuracy for vegetation classification.

• Extensive experiments use 5-fold cross validation.

• The experiments were conducted on dense and sparse grasses.

• In future, extension will be done by introducing large dataset of grasses.

摘要

•Proposed technique use LBP based GLCM feature vector and multiple classifiers.•We achieve over 92% accuracy for vegetation classification.•Extensive experiments use 5-fold cross validation.•The experiments were conducted on dense and sparse grasses.•In future, extension will be done by introducing large dataset of grasses.

论文关键词：Feature extraction,Support vector machine,k-Nearest Neighbor,Neural network,Hybrid technique

论文评审过程：Available online 3 March 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.02.047