R2FP: Rich and robust feature pooling for mining visual data

R2FP: Rich and robust feature pooling for mining visual data

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 a novel pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling (R2FP), to bette...

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Bibliographic Details
Main Authors: XIONG, Wei, DU, Bo, ZHANG, Lefei, HU, Ruimin, BIAN, Wei, SHEN, Jialie, TAO, Dacheng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
Subjects:
Autoencoder
Pooling
Representation learning
Computer Sciences
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/3539
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Institution: Singapore Management University
Language: English
Description
Summary: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 a novel pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling (R2FP), to better explore rich and robust representation from sparse feature maps of the input data. Both local and global pooling strategies are further considered to instantiate such a method and intensively studied. The former selects the most conductive features in the sub-region and summarizes the joint distribution of the selected features, while the latter is utilized to extract multiple resolutions of features and fuse the features with a feature balancing kernel for rich representation. Extensive experiments on several image recognition tasks demonstrate the superiority of the proposed techniques.

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