Learning simultaneous adaptive clustering and classification via MOEA
作者：
Highlights：
• A simultaneous adaptive clustering and classification learning via MOEA is proposed.
• New clustering objective function is designed, and two objective functions complement with each other.
• The number of clusters can be determined adaptively during the learning process.
• The drawback from clustering/classification learning is used to guide the search.
• We extend it to texture image and SAR image segmentation to show its effectiveness.
摘要
Highlights•A simultaneous adaptive clustering and classification learning via MOEA is proposed.•New clustering objective function is designed, and two objective functions complement with each other.•The number of clusters can be determined adaptively during the learning process.•The drawback from clustering/classification learning is used to guide the search.•We extend it to texture image and SAR image segmentation to show its effectiveness.
论文关键词：Multiobjective optimization,Evolutionary algorithm,Clustering learning,Classification learning,Simultaneous learning,Image segmentation
论文评审过程：Received 20 April 2015, Revised 5 May 2016, Accepted 15 May 2016, Available online 24 May 2016, Version of Record 31 May 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.05.004
