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UEC at TRECVID 2009 High Level Feature Task

By Zhiyuan Tang, Akitsugu Noguchi and Keiji Yanai

Abstract

In this paper, we describe our approach and results for high-level feature extraction task (HLF) at TRECVID2009. This year, we focus on fusion of a number of features effectively. Color, local pattern, texture, face, motion, and text were extracted from the video data. After that, an AP-weighted fusion and Multiple Kernel Learning were applied as a fusion method to combine all these features. Our submitted runs are as follows: • (Run1) UEC.APW fusion of six kinds of features, color, texture, face, motion, text and Bag-of-Features (BoF) model of local pattern features, by using the AP-weighted fusion. • (Run2) UEC.mkl 10, (Run3) UEC.mkl 100, (Run4) UEC.mkl50 100, (Run5) UEC.mkl100 10 fusion of six kinds of the features which the same as Run1 by using Multiple Kernel Learnin

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.189.1621
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