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Jointly learning heterogeneous features for RGB-D activity recognition

Jointly learning heterogeneous features for RGB-D activity recognition

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Authors

  • Jian-Fang Hu
  • Wei-Shi Zheng (Lead / Corresponding author)
  • Jianhuang Lai
  • Jianguo Zhang

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Original languageEnglish
Pages (from-to)5344-5352
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume39
Issue number11
Early online date15 Dec 2016
DOIs
StatePublished - Nov 2017
EventThe IEEE Conference on Computer Vision and Pattern Recognition - Boston, United States

Abstract

In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition. Considering that features from different channels could share some similar hidden structures, we propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogenous multi-task learning. The proposed model in an unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to enable the multi-task classifier learning, and 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by two inference models. Extensive results on three activity datasets have demonstrated the efficacy of the proposed method. In addition, a novel RGB-D activity dataset focusing on human-object interaction is collected for evaluating the proposed method, which will be made available to the community for RGB-D activity benchmarking and analysis

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    Accepted author manuscript, 7 MB, PDF-document

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