Compressive Sequential Learning for Action Similarity Labeling

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)
Official URL: http://dx.doi.org/10.1109/TIP.2015.2508600

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: 06 Feb 2017 11:05
URI: http://nrl.northumbria.ac.uk/id/eprint/29523

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence