کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10139637 | 1645970 | 2018 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A new framework of action recognition with discriminative parts, spatio-temporal and causal interaction descriptors
ترجمه فارسی عنوان
یک چارچوب جدید تشخیص عمل با بخش های تبعیض آمیز، توصیفگرهای متقابل فضایی-زمانی و علیت
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کلمات کلیدی
تشخیص عمل، خوشه طیفی، محدودیت تبعیض آمیز، بخش اقدام، ارتباط فاشیو-زمانی، رابطه علت،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
To improve action recognition performance, a novel discriminative spectral clustering method is firstly proposed, by which the candidate parts with the internal trajectories being close in spatial position, consistent in appearance and similar in motion velocity are mined. Furthermore, the discriminative constraint is introduced to select discriminative parts. Meanwhile, by fully considering the local and global distributions of data, a new similarity matrix is constructed, which enhances clustering effect. Secondly, the spatio-temporal interaction descriptor and causal interaction descriptor are constructed respectively, which fully mine the spatio-temporal and implicit causal interactive relationships between parts. Finally, a new framework is proposed. By associating the discriminative parts, spatio-temporal and causal interaction descriptors together as the inputs of Latent Support Vector Machine (LSVM), the correlations between action categories and action parts as well as interaction descriptors are mined. Consequently, accuracy is enhanced. The extensive and adequate experiments demonstrate the effectiveness of the proposed method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 56, October 2018, Pages 116-130
Journal: Journal of Visual Communication and Image Representation - Volume 56, October 2018, Pages 116-130
نویسندگان
Ming Tong, Yiran Chen, Mengao Zhao, Weijuan Tian,