HEp-2 cell classification with Vector of Hierarchically Aggregated Residuals
作者：
Highlights：
• A novel method for encoding features’ Sparse Representation residuals is proposed.
• Multiple levels of unsupervised learning are utilized.
• Local gradient descriptors are encoded into fixed length vectors.
• The method is evaluated on the task of HEp-2 classification.
• The proposed framework follows recent trends on feature encoding based on residuals.
摘要
•A novel method for encoding features’ Sparse Representation residuals is proposed.•Multiple levels of unsupervised learning are utilized.•Local gradient descriptors are encoded into fixed length vectors.•The method is evaluated on the task of HEp-2 classification.•The proposed framework follows recent trends on feature encoding based on residuals.
论文关键词：HEp-2 cell classification,Feature encoding,Feature aggregation,Sparse representation
论文评审过程：Received 22 May 2016, Revised 20 November 2016, Accepted 14 December 2016, Available online 15 December 2016, Version of Record 20 December 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.12.013
