TY - GEN
T1 - Automatic Web Video Categorization Using Audio-Visual Information and Hierarchical Clustering RF
AU - Ionescu, B.
AU - Seyerlehner, Klaus
AU - Mironica, I.
AU - Vertan, C.
AU - Lambert, P.
PY - 2012
Y1 - 2012
N2 - In this paper, we discuss and audio-visual approach to automatic
web video categorization. We propose content descriptors
which exploit audio, temporal, and color content. The
power of our descriptors was validated both in the context of
a classification system and as part of an information retrieval
approach. For this purpose, we used a real-world scenario,
comprising 26 video categories from the blip.tv media platform
(up to 421 hours of video footage). Additionally, to
bridge the descriptor semantic gap, we propose a new relevance
feedback technique which is based on hierarchical clustering.
Experiments demonstrated that retrieval performance
can be increased significantly and becomes comparable to that
of high level semantic textual descriptors.
AB - In this paper, we discuss and audio-visual approach to automatic
web video categorization. We propose content descriptors
which exploit audio, temporal, and color content. The
power of our descriptors was validated both in the context of
a classification system and as part of an information retrieval
approach. For this purpose, we used a real-world scenario,
comprising 26 video categories from the blip.tv media platform
(up to 421 hours of video footage). Additionally, to
bridge the descriptor semantic gap, we propose a new relevance
feedback technique which is based on hierarchical clustering.
Experiments demonstrated that retrieval performance
can be increased significantly and becomes comparable to that
of high level semantic textual descriptors.
UR - https://www.scopus.com/pages/publications/84869776199
M3 - Conference proceedings
SN - 9781467310680
T3 - European Signal Processing Conference
SP - 375
EP - 379
BT - Proceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
ER -