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The Scientific World Journal
Volume 2014, Article ID 219732, 12 pages
http://dx.doi.org/10.1155/2014/219732
Research Article

Secure Access Control and Large Scale Robust Representation for Online Multimedia Event Detection

1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
2School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
3School of Computer Science, Wuyi University, Jiangmen 529020, China
4State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, China

Received 2 April 2014; Accepted 30 June 2014; Published 22 July 2014

Academic Editor: Vincenzo Eramo

Copyright © 2014 Changyu Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.