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

      research-article
      1 , 2 , 3 , * , 4
      The Scientific World Journal
      Hindawi Publishing Corporation

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          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.

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          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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            Sparse multinomial logistic regression: fast algorithms and generalization bounds.

            Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly large or exactly zero. From a learning-theoretic perspective, these methods control the capacity of the learned classifier by minimizing the number of basis functions used, resulting in better generalization. This paper presents three contributions related to learning sparse classifiers. First, we introduce a true multiclass formulation based on multinomial logistic regression. Second, by combining a bound optimization approach with a component-wise update procedure, we derive fast exact algorithms for learning sparse multiclass classifiers that scale favorably in both the number of training samples and the feature dimensionality, making them applicable even to large data sets in high-dimensional feature spaces. To the best of our knowledge, these are the first algorithms to perform exact multinomial logistic regression with a sparsity-promoting prior. Third, we show how nontrivial generalization bounds can be derived for our classifier in the binary case. Experimental results on standard benchmark data sets attest to the accuracy, sparsity, and efficiency of the proposed methods.
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              Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition

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                Author and article information

                Journal
                ScientificWorldJournal
                ScientificWorldJournal
                TSWJ
                The Scientific World Journal
                Hindawi Publishing Corporation
                2356-6140
                1537-744X
                2014
                22 July 2014
                : 2014
                : 219732
                Affiliations
                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
                Author notes

                Academic Editor: Vincenzo Eramo

                Author information
                http://orcid.org/0000-0002-7449-2269
                Article
                10.1155/2014/219732
                4132300
                7cc5b68c-c308-4705-8aad-2f41b9b64ed8
                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.

                History
                : 2 April 2014
                : 30 June 2014
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