R2FP: Rich and robust feature pooling for mining visual data

Wei Xiong, Bo Du, Lefei Zhang, Ruimin Hu, Wei Bian, Jialie Shen, Dacheng Tao

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

Abstract

The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose anovel pooling method within an unsupervised feature learningframework, named Rich and Robust Feature Pooling (R2FP), to better explore rich and robust representation from sparsefeature maps of the input data. Both local and global poolingstrategies are further considered to instantiate such a methodand intensively studied. The former selects the most conductivefeatures in the sub-region and summarizes the joint distributionof the selected features, while the latter is utilized to extractmultiple resolutions of features and fuse the features witha feature balancing kernel for rich representation. Extensiveexperiments on several image recognition tasks demonstratethe superiority of the proposed techniques.

Original languageEnglish
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
Place of PublicationAtlantic City; United States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages469-478
Number of pages10
ISBN (Electronic)9781467395038
DOIs
Publication statusPublished - Nov 2015
Externally publishedYes
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: 14 Nov 201517 Nov 2015

Conference

Conference15th IEEE International Conference on Data Mining, ICDM 2015
CountryUnited States
CityAtlantic City
Period14/11/1517/11/15

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  • Cite this

    Xiong, W., Du, B., Zhang, L., Hu, R., Bian, W., Shen, J., & Tao, D. (2015). R2FP: Rich and robust feature pooling for mining visual data. In Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015 (pp. 469-478). [15699684] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDM.2015.98