Max‐margin discriminative random fields for multimodal human action recognition. Issue 12 (1st June 2014) - Su, Yuting; Ma, Li; Liu, An‐An; Yang, Zhaoxuan
Abstract : Proposed is a max‐margin discriminative random fields model for multimodal human action recognition. To incorporate multiple modalities for joint modelling, a specific graphical structure with parallel sequential observations and related hidden‐state layers is designed. Moreover, the corresponding potential functions for model formulation are designed. For model learning, the max‐margin learning method is proposed to discover both latent correlation among multimodal data and temporal context within individual modality. A comparison experiment shows that the proposed model can boost the performance of human action recognition by taking advantage of complementary characteristics from multiple modalities.