NDLI: Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach

Content Provider	Springer Nature Link
Author	Liu, Huaping Sun, Fuchun
Copyright Year	2016
Abstract	Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods.
Starting Page	519
Ending Page	528
Page Count	10
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	8
Issue Number	3
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2016-01-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Topical object discovery Dictionary learning Video summarization Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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