Qin, Jie, Liu, Li, Zhang, Zhaoxiang, Wang, Yunhong and Shao, Ling (2016) Compressive Sequential Learning for Action Similarity Labeling. IEEE Transactions on Image Processing, 25 (2). pp. 756-769. ISSN 1057-7149
Full text not available from this repository. (Request a copy)Abstract
Human action recognition in videos has been extensively studied in recent years due to its wide range of applications. Instead of classifying video sequences into a number of action categories, in this paper, we focus on a particular problem of action similarity labeling (ASLAN), which aims at verifying whether a pair of videos contain the same type of action or not. To address this challenge, a novel approach called compressive sequential learning (CSL) is proposed by leveraging the compressive sensing theory and sequential learning. We first project data points to a low-dimensional space by effectively exploring an important property in compressive sensing: the restricted isometry property. In particular, a very sparse measurement matrix is adopted to reduce the dimensionality efficiently. We then learn an ensemble classifier for measuring similarities between pairwise videos by iteratively minimizing its empirical risk with the AdaBoost strategy on the training set. Unlike conventional AdaBoost, the weak learner for each iteration is not explicitly defined and its parameters are learned through greedy optimization. Furthermore, an alternative of CSL named compressive sequential encoding is developed as an encoding technique and followed by a linear classifier to address the similarity-labeling problem. Our method has been systematically evaluated on four action data sets: ASLAN, KTH, HMDB51, and Hollywood2, and the results show the effectiveness and superiority of our method for ASLAN.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | sparse random projection, action recognition, action similarity labeling, pair matching, boosting |
Subjects: | G400 Computer Science |
Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
Depositing User: | Becky Skoyles |
Date Deposited: | 06 Feb 2017 11:05 |
Last Modified: | 12 Oct 2019 22:26 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/29523 |
Downloads
Downloads per month over past year