Locality-constrained affine subspace coding for image classification and retrieval

作者：

Highlights：

• We propose a novel feature coding method based on a dictionary of affine subspaces in terms of feature reconstruction and statistics modeling.

• The proposed method models the first- and second-order information, while preserving the properties of locality and sparsity of coding vectors.

• Extensive experiments show the proposed method performs on par with or better than the state-of-the-arts for image classification and retrieval.

摘要

•We propose a novel feature coding method based on a dictionary of affine subspaces in terms of feature reconstruction and statistics modeling.•The proposed method models the first- and second-order information, while preserving the properties of locality and sparsity of coding vectors.•Extensive experiments show the proposed method performs on par with or better than the state-of-the-arts for image classification and retrieval.

论文关键词：Bag of visual words,Locality-constrained affine subspace coding,Image classification,Image retrieval

论文评审过程：Received 19 March 2018, Revised 31 October 2019, Accepted 15 December 2019, Available online 24 December 2019, Version of Record 30 December 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.107167