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With the exploding growth of data, the computational complexity required by learning Support Vector Machine (SVM) lays a heavy burden on real-world applications. To address this issue, parallel computational techniques can be employed such as the Graphics Processing Units (GPUs) and MapReduce model. As it is well known, GPUs are microprocessors on a multi-core architecture which reveal high performance...
This paper presents a large-scale image retrieval system based on an efficient Graphics Processing Units (GPU)-based MapReduce framework for the MSR-Bing Image Retrieval Challenge. The proposed system is designed for searching images and scoring image-query pairs based on their relevances efficiently and accurately. Unlike the former systems which usually start with text queries to select partial...
With the exponential growth of multimedia data, people are overwhelmed with massive amount of online videos, of which Near-Duplicate Videos (NDVs) occupy a large portion. In this paper, we present a novel framework for NDV retrieval, which explores the parallel power of two promising techniques: Graphics Processing Unit (GPU) and MapReduce. With the power of the proposed framework, various key algorithms...
In this paper, the framework of MapReduce is explored for large-scale multimedia data mining. Firstly, a brief overview of MapReduce and Hadoop is presented to speed up large-scale multimedia data mining. Then, the high-level theory and low-level implementation for several key computer vision technologies involved in this work are introduced, such as 2D/3D interest point detection, clustering, bag...
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