Jointly learning heterogeneous features for RGB-D activity recognition

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

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)
457 Downloads (Pure)


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
Original languageEnglish
Pages (from-to)2186-2200
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number11
Early online date15 Dec 2016
Publication statusPublished - Nov 2017
EventThe IEEE Conference on Computer Vision and Pattern Recognition - USA, Boston, United States
Duration: 7 Jun 201512 Jun 2015


  • heterogeneous features learning
  • RGB-D activity recognition
  • action recognition


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