کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4948149 | 1439609 | 2016 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel mid-level distinctive feature learning for action recognition via diffusion map
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Recent works have shown that mid-level feature is superior to low-level feature, which can not only improve discriminative power, but also enhance descriptive capability. In this paper, the classical STIP, spatial star-graph and temporal star-graph are first extracted to represent human action from multi-perspectives. Then a principled feature learning algorithm is proposed to embed these multi-cues into a unified space and enhance all low-level features using diffusion map. Unlike treating spatio-temporal patch as mid-level primitive, we use a graph to model different types of primitives, then apply graph partitioning to co-cluster them into visual-word clusters called mid-level distinctive feature, which can bridge the semantic gap across low-level features. Experimental results show that our approach can successfully classify human activities with higher accuracies both on single-person actions (KTH and UCF) and complex interactional activities (UT-Interaction and HMDB51).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 218, 19 December 2016, Pages 185-196
Journal: Neurocomputing - Volume 218, 19 December 2016, Pages 185-196
نویسندگان
Wanru Xu, Zhenjiang Miao, Yi Tian,