A new parallel video understanding and retrieval system
ABSTRACT In this paper, a hybrid parallel computing framework is proposed for video understanding and retrieval. It is a unified computing architecture based on the Map-Reduce programming model, which supports multi-core and GPU architectures. A key task scheduler is designed for the parallelization of computation tasks. The SVM method is used to train models for video understanding purposes. To effectively shorten the training and processing time, the hybrid computing framework is used to train large scale SVM models. The TRECVID database is used as the basic experimental content for video understanding and retrieval. Experiments were conducted on two 8-core servers, each equipped with NVIDIA Quadro FX 4600 graphics card. Results proved that the proposed parallel computing framework works well for the video understanding and retrieval system by speeding up system development and providing better performances.
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ABSTRACT: . The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for a restricted case. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing RankBoost to nearest-neighbor and regression algorithms. 1 Introduction Consider the followingmovie-recommendat...Journal of Machine Learning Research 01/2003; 4:933-969. · 3.42 Impact Factor
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ABSTRACT: Parallel software for solving the quadratic program arising in training support vector machines for classification problems is introduced. The software implements an iterative decomposition technique and exploits both the storage and the computing resources available on multiprocessor systems, by distributing the heaviest computational tasks of each decomposition iteration. Based on a wide range of recent theoretical advances, relevant decomposition issues, such as the quadratic subproblem solution, the gradient updating, the working set selection, are systematically described and their careful combination to get an effective parallel tool is discussed. A comparison with state-of- the-art packages on benchmark problems demonstrates the good accuracy and the remarkable time saving achieved by the proposed software. Furthermore, challenging experiments on real-world data sets with millions training samples highlight how the software makes large scale standard nonlinear support vector machines effectively tractable on common multiprocessor systems. This feature is not shown by any of the available codes. Keywords: support vector machines, large scale quadratic programs, decomposition techniques, gradient projection methods, parallel computationJournal of Machine Learning Research 01/2006; 7:1467-1492. · 3.42 Impact Factor
Conference Paper: Parallelizing Support Vector Machines on Distributed Computers.[Show abstract] [Hide abstract]
ABSTRACT: Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation. Let n denote the number of training instances, p the reduced matrix dimension after factorization (p is significantly smaller than n), and m the number of machines. PSVM reduces the memory requirement from O(n2) to O(np=m), and improves computation time to O(np2=m). Empirical study shows PSVM to be effective. PSVM Open Source is available for download at http://code.google.com/p/psvm/.Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007; 01/2007