NDLI: Video Semantic Concept Detection Based on Multi-modality Fusion

Content Provider	IEEE Xplore Digital Library
Author	Zhao Jianxun Wu Bo
Copyright Year	2012
Abstract	Multiple kernel learning methods have a widespread application in visual concept learning and BoVW method has been widely used dues to its excellent categorization performance. However, most canonical multiple kernel learning methods employ a stationary kernel combination format which assigns a uniform kernel weights over the input space. And BoVW method aimed to resolve the problem that the time efficiency of BoVW method decreases as the visual data scales up. As it is true for human perception, learning from multi-modalities has become an effective scheme for various information retrieval problems. In this paper, we propose a novel multi-modality fusion approach for video search, where the search modalities are derived from a diverse set of knowledge sources. Our proposed approach, explores a large set of predefined semantic concepts for computing multi-modality fusion weights by a new method. Experimental results validate the effectiveness of our approach, which outperforms the existing multi-modality fusion methods.
Starting Page	334
Ending Page	338
File Size	180064
Page Count	5
File Format	PDF
ISBN	9781467306898
e-ISBN	9780769546476
DOI	10.1109/ICCSEE.2012.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Visualization Dictionaries Semantics Inter-Class Correlation Multi-modality Feature extraction Clustering Kernel Visual Semantic Concept
Content Type	Text
Resource Type	Article

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