NDLI: A New Spatio-Temporal Saliency-Based Video Object Segmentation

Content Provider	Springer Nature Link
Author	Tu, Zhengzheng Abel, Andrew Zhang, Lei Luo, Bin Hussain, Amir
Copyright Year	2016
Abstract	Humans and animals are able to segment visual scenes by having the natural cognitive ability to quickly identify salient objects in both static and dynamic scenes. In this paper, we present a new spatio-temporal-based approach to video object segmentation that considers both motion- and image-based saliency to produce a weighted approach which can segment both static and dynamic objects. We perform fast optical flow and then calculate the motion saliency based on this temporal information, detecting the presence of global motion and adjusting the initial optical flow results accordingly. This is then fused with a region-based contrast image saliency method, with both techniques weighted. Finally, our joint weighted saliency map is used as part of a foreground–background labelling approach to produce the final segmented video files. Good results in a wide range of environments are presented, showing that our spatio-temporal system is more robust and consistent than a number of other state-of-the-art approaches.
Starting Page	629
Ending Page	647
Page Count	19
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	8
Issue Number	4
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2016-03-08
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Video object segmentation Global motion Spatio-temporal saliency Foreground–background labelling 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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