Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Combined Feature-Level Video Indexing Using Block-Based Motion Estimation.
AU - Bhaskar, H.
AU - Mihaylova, L.
N1 - Catalogue number: CFP10FUS-CDR ISBN:978-0-9824438-1-1
PY - 2010/7/28
Y1 - 2010/7/28
N2 - We describe a method for attaching content-based labels to video data using a weighted combination of low-level features (such as colour, texture, motion, etc.) estimated during motion analysis. Every frame of a video sequence is modeled using a fixed set of low-level feature attributes together with a set of corresponding weights using a block-based motion estimation technique. Indexing a new video involves an alternative scheme in which the weights of the features are first estimated and then classification is performed to determine the label corresponding to the video. A hierarchical architecture of increasingly complexity is used to achieve robust indexing of new videos. We explore the effect of different model parameters on performance and prove that the proposed method is effective using publicly available datasets.
AB - We describe a method for attaching content-based labels to video data using a weighted combination of low-level features (such as colour, texture, motion, etc.) estimated during motion analysis. Every frame of a video sequence is modeled using a fixed set of low-level feature attributes together with a set of corresponding weights using a block-based motion estimation technique. Indexing a new video involves an alternative scheme in which the weights of the features are first estimated and then classification is performed to determine the label corresponding to the video. A hierarchical architecture of increasingly complexity is used to achieve robust indexing of new videos. We explore the effect of different model parameters on performance and prove that the proposed method is effective using publicly available datasets.
KW - Tracking
KW - filtering
KW - estimation
KW - fuzzy logic
KW - resource management
M3 - Conference contribution/Paper
SN - 978-0-9824438-1-1
SP - 1
EP - 8
BT - 13th Conference on Information Fusion (FUSION), 2010
PB - IEEE
T2 - 13th International Conference on Information Fusion
Y2 - 26 July 2010 through 29 July 2010
ER -