期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 26, 期 2, 页码 738-750出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2016.2621664
关键词
Sparse canonical temporal alignment; key frames; deep non-negative tensor factorization; tensor-train
资金
- NSF IIS Award [1651902]
- NSF CNS Award [1314484]
- ONR Award [N00014-12-1-1028, N00014-14-1-0484]
- U.S. Army Research Office [W911NF-14-1-0218]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1314484] Funding Source: National Science Foundation
In this paper, we solve three problems in action recognition: sub-action, multi-subject, and multi-modality, by reducing the diversity of intra-class samples. The main stage contains canonical temporal alignment and key frames selection. As we know, temporal alignment aims to reduce the diversity of intra-class samples; however, dense frames may yield misalignment or overlapped alignment and decrease recognition performance. To overcome this problem, we propose a sparse canonical temporal alignment (SCTA) method, which selects and aligns key frames from two sequences to reduce diversity. To extract better features from the key frames, we propose a deep non-negative tensor factorization (DNTF) method to find a tensor subspace integrated with SCTA scheme. First, we model an action sequence as a third-order tensor with spatiotemporal structure. Then, we design a DNTF scheme to find a tensor subspace in both spatial and temporal directions. Particularly, in the first layer, the original tensor is decomposed into two low-rank tensors by NTF, and in the second layer, each low-rank tensor is further decomposed by tensor-train for time efficiency. Finally, our framework composed of SCTA and DNTF could solve the three problems and extract effective features for action recognition. Experiments on synthetic data, MSRDailyActivity3D, and MSRActionPairs data sets show that our method works better than competitive methods in terms of accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据