NDLI: Recognizing key segments of videos for video annotation by learning from web image sets

Content Provider	Springer Nature Link
Author	Song, Hao Wu, Xinxiao Liang, Wei Jia, Yunde
Copyright Year	2016
Abstract	In this paper, we propose an approach of inferring the labels of unlabeled consumer videos and at the same time recognizing the key segments of the videos by learning from Web image sets for video annotation. The key segments of the videos are automatically recognized by transferring the knowledge learned from related Web image sets to the videos. We introduce an adaptive latent structural SVM method to adapt the pre-learned classifiers using Web image sets to an optimal target classifier, where the locations of the key segments are modeled as latent variables because the ground-truth of key segments are not available. We utilize a limited number of labeled videos and abundant labeled Web images for training annotation models, which significantly alleviates the time-consuming and labor-expensive collection of a large number of labeled training videos. Experiment on the two challenge datasets Columbia’s Consumer Video (CCV) and TRECVID 2014 Multimedia Event Detection (MED2014) shows our method performs better than state-of-art methods.
Starting Page	6111
Ending Page	6126
Page Count	16
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	76
Issue Number	5
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2016-02-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Video annotation Key segment Image set Transfer learning Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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