NDLI: Multimodal feature fusion for robust event detection in web videos

Content Provider	IEEE Xplore Digital Library
Author	Natarajan, P. Shuang Wu Vitaladevuni, S. Xiaodan Zhuang Tsakalidis, S. Unsang Park Prasad, R. Natarajan, P.
Copyright Year	2012
Description	Author affiliation: Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA 02138 (Natarajan, P.; Shuang Wu; Vitaladevuni, S.; Xiaodan Zhuang; Tsakalidis, S.; Unsang Park; Prasad, R.; Natarajan, P.)
Abstract	Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance, color, motion, audio and audio-visual co-occurrence patterns in videos. We also evaluate the utility of high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Further, we exploit multimodal information by analyzing available spoken and videotext content using state-of-the-art automatic speech recognition (ASR) and videotext recognition systems. We combine these diverse features using a two-step strategy employing multiple kernel learning (MKL) and late score level fusion methods. Based on the TRECVID MED 2011 evaluations for detecting 10 events in a large benchmark set of ∼45000 videos, our system showed the best performance among the 19 international teams.
Starting Page	1298
Ending Page	1305
File Size	388091
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247814
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Videos Feature extraction Speech Vectors Kernel Encoding Image color analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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