TY - GEN
T1 - Integration of mapreduce with an interactive boosting mechanism for image background subtraction in cultural sightseeing
AU - Cheng, Sheng Tzong
AU - Chen, Yin Jun
AU - Wang, Yu Ting
AU - Chen, Chen Fei
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84961376241&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961376241&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-46315-4_19
DO - 10.1007/978-3-662-46315-4_19
M3 - Conference contribution
AN - SCOPUS:84961376241
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 180
EP - 191
BT - Advances in Web-Based Learning - ICWL 2013 Workshops - USL 2013, IWSLL 2013, KMEL 2013, IWCWL 2013, WIL 2013, and IWEEC 2013, Revised Selected Papers
A2 - Li, Qing
A2 - Lau, Rynson
A2 - Chiu, Dickson K.W.
A2 - Shih, Timothy K.
A2 - Yang, Chu-Sing
A2 - Popescu, Elvira
A2 - Wang, Minhong
A2 - Sampson, Demetrios G.
PB - Springer Verlag
T2 - 12th International Conference on Web-Based Learning, ICWL 2013, held with 1st International Workshop on Ubiquitous Social Learning, USL 2013, International Workshop on Smart Living and Learning, IWSLL 2013, International Workshop on Cloud Computing for Web-Based Learning, IWCWL 2013, International Workshop on Web Intelligence and Learning, WIL 2013, International Workshop on E-book and Education Cloud, IWEEC 2013 and 3rd International Symposium on Knowledge Management and E-Learning, KMEL 2013
Y2 - 6 October 2013 through 9 October 2013
ER -