کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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534486 | 870257 | 2015 | 7 صفحه PDF | دانلود رایگان |
The classification of multi-modal data has been an active research topic in recent years. It has been used in many applications where the processing of multi-modal data is involved. Motivated by the assumption that different modalities in multi-modal data share latent structure (topics), this paper attempts to learn the shared structure by exploiting the symbiosis of multiple-modality and therefore boost the classification of multi-modal data, we call it Multi-modal Hidden Conditional Random Field (M-HCRF). M-HCRF represents the intrinsical structure shared by different modalities as hidden variables in a undirected general graphical model. When learning the latent shared structure of the multi-modal data, M-HCRF can discover the interactions among the hidden structure and the supervised category information. The experimental results show the effectiveness of our proposed M-HCRF when applied to the classification of multi-modal data.
Journal: Pattern Recognition Letters - Volume 51, 1 January 2015, Pages 63–69