A novel localized and second order feature coding network for image recognition

作者：

Highlights：

• We propose an end-to-end model called localized and second-order VLAD Network (LSO-VLADNet) for image recognition.

• The proposed network uses an end-to-end dimension reduction layer to ensure the learned feature has low dimension.

• The back-propagation models of all the layers are obtained, and the entire network is trained by the end-to-end manner.

• Experiments on four image databases demonstrate that the proposed network is very competitive.

摘要

•We propose an end-to-end model called localized and second-order VLAD Network (LSO-VLADNet) for image recognition.•The proposed network uses an end-to-end dimension reduction layer to ensure the learned feature has low dimension.•The back-propagation models of all the layers are obtained, and the entire network is trained by the end-to-end manner.•Experiments on four image databases demonstrate that the proposed network is very competitive.

论文关键词：Deep neural network,Feature coding network,Localized and second order VLAD,End to end,Image recognition

论文评审过程：Received 14 June 2017, Revised 24 September 2017, Accepted 30 October 2017, Available online 9 November 2017, Version of Record 21 November 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.039