By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- Investigators publish new report on Computation - Neural Computation. According to news reporting originating in Tianjin, People’s Republic of China, by VerticalNews journalists, research stated, “Although research on detection of saliency and visual attention has been active over recent years, most of the existing work focuses on still image rather than video based saliency. In this paper, a deep learning based hybrid spatiotemporal saliency feature extraction framework is proposed for saliency detection from video footages.”
Financial supporters for this research include National Natural Science Foundation, China, Royal Society of Edinburgh and NSFC.
The news reporters obtained a quote from the research from Tianjin University, “The deep learning model is used for the extraction of high-level features from raw video data, and they are then integrated with other high-level features. The deep learning network has been found extremely effective for extracting hidden features than that of conventional handcrafted methodology. The effectiveness for using hybrid high-level features for saliency detection in video is demonstrated in this work. Rather than using only one static image, the proposed deep learning model take several consecutive frames as input and both the spatial and temporal characteristics are considered when computing saliency maps. The efficacy of the proposed hybrid feature framework is evaluated by five databases with human gaze complex scenes. Experimental results show that the proposed model outperforms five other state-of-the-art video saliency detection approaches. In addition, the proposed framework is found useful for other video content based applications such as video highlights.”
According to the news reporters, the research concluded: “As a result, a large movie clip dataset together with labeled video highlights is generated.”
For more information on this research see: A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing , 2018;287():68-83. Neurocomputing can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Neurocomputing - http://www.journals.elsevier.com/neurocomputing/)
Our news correspondents report that additional information may be obtained by contacting Z. Wang, Tianjin University, Sch Comp Software, MTS Lab, Tianjin 300350, People’s Republic of China. Additional authors for this research include J.C. Ren, D. Zhang, M.J. Sun and J.M. Jiang.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.neucom.2018.01.076. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-09), Studies from Tianjin University Provide New Data on Neural Computation (A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos), Journal of Robotics & Machine Learning, 272, ISSN: 1944-186X, BUTTER® ID: 015467872
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