NDLI: Discovering compact topical descriptors for web video retrieval

Content Provider	IEEE Xplore Digital Library
Author	Fang Zhao Yongzhen Huang Liang Wang Tieniu Tan
Copyright Year	2013
Description	Author affiliation: Center for Res. on Intell. Perception & Comput., Nat. Lab. of Pattern Recognition, Beijing, China (Fang Zhao; Yongzhen Huang; Liang Wang; Tieniu Tan)
Abstract	Describing videos efficiently is an important task for content based web video retrieval. To solve this problem, we propose an unsupervised approach based on an undirected topic model to learn a compact topical descriptor upon the bag-of-words (BoW) video representation. In our method, words in a BoW are assumed to have different topic features, and the topical descriptor of an entire video is obtained by aggregating those features, which makes the descriptor contain information about relative strength of topics. To improve the descriptor interpretability, an $L_{1}$ penalty is used to control the topical sparsity. Furthermore, efficient learning and inference algorithms are presented. We evaluate the proposed descriptor on the Columbia Consumer Video dataset. Experimental results demonstrate that compared with the BoW and other topical representations, the proposed compact descriptor has better performance in web video retrieval.
Starting Page	2679
Ending Page	2683
File Size	283870
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738552
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	sparse representation Web video retrieval compact topical descriptor undirected topic model
Content Type	Text
Resource Type	Article

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