TY - GEN
T1 - VideoMule
T2 - 17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
AU - Ramachandran, Chandrasekar
AU - Malik, Rahul
AU - Jin, Xin
AU - Gao, Jing
AU - Nahrstedt, Klara
AU - Han, Jiawei
PY - 2009
Y1 - 2009
N2 - With the growing proliferation of conversational media and devices for generating multimedia content, the Internet has seen an expansion in websites catering to user-generated media. Most of the user-generated content is multimodal in nature as it has videos, audio, text (in the form of tags), comments and so on. Content analysis is a challenging problem on this type of media since it is noisy, unstructured and unreliable. In this paper we propose VideoMule, a consensus learning approach for multi-label video classification from noisy user-generated videos. In our scheme, we train classification and clustering algorithms on individual modes of information such as user comments, tags, video features and so on. We then combine the results of trained classifiers and clustering algorithms using a novel heuristic consensus learning algorithm which as a whole performs better than each individual learning model.
AB - With the growing proliferation of conversational media and devices for generating multimedia content, the Internet has seen an expansion in websites catering to user-generated media. Most of the user-generated content is multimodal in nature as it has videos, audio, text (in the form of tags), comments and so on. Content analysis is a challenging problem on this type of media since it is noisy, unstructured and unreliable. In this paper we propose VideoMule, a consensus learning approach for multi-label video classification from noisy user-generated videos. In our scheme, we train classification and clustering algorithms on individual modes of information such as user comments, tags, video features and so on. We then combine the results of trained classifiers and clustering algorithms using a novel heuristic consensus learning algorithm which as a whole performs better than each individual learning model.
KW - Multimodal information processing
KW - Video classification
UR - http://www.scopus.com/inward/record.url?scp=72449149179&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72449149179&partnerID=8YFLogxK
U2 - 10.1145/1631272.1631397
DO - 10.1145/1631272.1631397
M3 - Conference contribution
AN - SCOPUS:72449149179
SN - 9781605586083
T3 - MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
SP - 721
EP - 724
BT - MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
Y2 - 19 October 2009 through 24 October 2009
ER -