ELM based signature for texture classification
作者：
Highlights：
• In this study, we model the texture as an Extreme Learning Machine (ELM).
• ELM is a single-hidden layer feed forward neural network with a very fast learning algorithm.
• We divided the image into small windows to compute the input and label of the ELM.
• Pixels of each window are the input, while the central pixel is the label of the ELM.
• We use the output weights of the ELM as a feature vector for texture classification.
摘要
Highlights•In this study, we model the texture as an Extreme Learning Machine (ELM).•ELM is a single-hidden layer feed forward neural network with a very fast learning algorithm.•We divided the image into small windows to compute the input and label of the ELM.•Pixels of each window are the input, while the central pixel is the label of the ELM.•We use the output weights of the ELM as a feature vector for texture classification.
论文关键词：Texture classification,Neural network,Extreme Learning Machine
论文评审过程：Received 4 February 2015, Revised 10 July 2015, Accepted 13 September 2015, Available online 28 September 2015, Version of Record 27 November 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.09.014
