Paper Info
Title
Integration of MapReduce with an Interactive Boosting Mechanism for Image Background Subtraction in Cultural Sightseeing.
Abstract
Background subtraction is widely used in multimedia applications, such as traffic monitoring, video surveillance, and object tracking. Several methods with different advantages in different applications have been proposed. The advent of cloud computing also has made possible of the combination of various background subtraction techniques and the processing of large amounts of images. In this paper, an integrated algorithm for background subtraction is implemented and analyzed. The proposed AdaBoost algorithm combines weak classifiers: pixel-based background subtraction methods, block-based background subtraction methods, and graph-cut segmentation methods. After training, the program adjusts the weight of each weak classifier. The algorithm is accelerated using Hadoop cloud-computing architecture. By using a MapReduce framework, this system can parallel-processing on multiple servers in order to reduce computing time. When the system completes its task, the user can see the combined results on the screen and then choose the preferred result. The system can obtain user feedback and tune the combination mechanism.
Year
DOI
Venue
2013
10.1007/978-3-662-46315-4_19
ADVANCES IN WEB-BASED LEARNING
Keywords
Field
DocType
Cloud computing,Background subtraction,Boosting learning
Background subtraction,Computer vision,Segmentation,Computer science,Server,Video tracking,Pixel,Artificial intelligence,Boosting (machine learning),Classifier (linguistics),Cloud computing
Conference
Volume
ISSN
Citations 
8390
0302-9743
0
PageRank 
References 
Authors
0.34
6
4
Name
Order
Citations
PageRank
Sheng-Tzong Cheng129344.23
Yin-Jun Chen221.37
Yu-Ting Wang3316.90
Chen-Fei Chen400.34