Title :
Large-scale multimedia data mining using MapReduce framework
Author :
Hanli Wang ; Yun Shen ; Lei Wang ; Kuangtian Zhufeng ; Wei Wang ; Cheng Cheng
Author_Institution :
Key Lab. of Embedded Syst. & Service Comput., Tongji Univ., Shanghai, China
Abstract :
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 of features, and so on. Experimental results on image classification, video event detection and near-duplicate video retrieval are carried out on a five-node Hadoop cluster to demonstrate the efficiency of the proposed MapReduce framework for large-scale multimedia data mining applications.
Keywords :
computer vision; data mining; image classification; multimedia computing; object detection; parallel programming; video retrieval; Hadoop; MapReduce; computer vision; image classification; multimedia data mining; near duplicate video retrieval; video event detection; Data mining; Event detection; Feature extraction; Multimedia communication; Streaming media; Training; Visualization; Hadoop; Image classification; MapReduce; Near-duplicate video retrieval; Video event detection;
Conference_Titel :
Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4673-4511-8
Electronic_ISBN :
978-1-4673-4509-5
DOI :
10.1109/CloudCom.2012.6427595