Content based image retrieval with sparse representations and local feature descriptors : A comparative study

作者：

Highlights：

• Content Based Image Retrieval (CBIR) with Sparse Representation (SR) and Local Feature Descriptors (LFDs) is investigated in detail.

• Popular LFDs are analyzed.

• Popular Dictionary Learning (DL) and Coefficient Learning (CL) algorithms are analyzed.

• A framework for CBIR is proposed to analyze the performances of LFD and SR algorithms.

• Best results of the framework for each analyzed dictionary are compared with CBIR approaches in literature.

摘要

•Content Based Image Retrieval (CBIR) with Sparse Representation (SR) and Local Feature Descriptors (LFDs) is investigated in detail.•Popular LFDs are analyzed.•Popular Dictionary Learning (DL) and Coefficient Learning (CL) algorithms are analyzed.•A framework for CBIR is proposed to analyze the performances of LFD and SR algorithms.•Best results of the framework for each analyzed dictionary are compared with CBIR approaches in literature.

论文关键词：Content based image retrieval,Local feature descriptor,Sparse representation,Dictionary learning,Coefficient learning

论文评审过程：Received 23 August 2016, Revised 10 February 2017, Accepted 3 March 2017, Available online 6 March 2017, Version of Record 8 March 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.03.006