Learning feature fusion strategies for various image types to detect salient objects

作者：

Highlights：

• Incorporated a novel input instance matching scheme in a learning classifier system.

• Effectively learned different feature combinations for various types of images.

• Outperformed nine individual feature based methods and seven combinatorial methods.

• The new method preserves more details of objects than the state-of-the-art methods.

摘要

Highlights•Incorporated a novel input instance matching scheme in a learning classifier system.•Effectively learned different feature combinations for various types of images.•Outperformed nine individual feature based methods and seven combinatorial methods.•The new method preserves more details of objects than the state-of-the-art methods.

论文关键词：Object Detection,Saliency Map,Learning Classifier Systems,XCS,Pattern Recognition

论文评审过程：Received 7 March 2016, Revised 20 April 2016, Accepted 4 May 2016, Available online 21 May 2016, Version of Record 1 June 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.05.020