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Visual saliency detection based on full convolution neural networks and center prior

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Video saliency detection aims to mimic the human's visual attention system of perceiving the world via extracting the most attractive regions or objects in the input video. At present, traditional video saliency-detection models have achieved good performance in many applications. However, it is still challenging in exploiting the consistency of spatiotemporal information. In order to tackle this challenge, this paper proposes a video saliency-detection model based on human attention mechanism and full convolution neural networks. First, visual features are extracted from video frames through the fully convolutional networks. The second stage is to spread attention features to the other layer (i. e. the fifth layer) of fully convolutional networks via a weight sharing strategy. Finally, the final result produced by the convolution network is optimized by considering spatial location information with center prior of the salient object. Experimental results show that the performance of the proposed algorithm is superior to other state-of-the-art methods based on the widely used data set for video saliency detection.
Original languageEnglish
Title of host publication2019 12th International Conference on Human System Interaction (HSI)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)978-1-7281-3980-7, 978-1-7281-3979-1
ISBN (Print)978-1-7281-3981-4
Publication statusPublished - 26 Dec 2019
Event 2019 12th International Conference on Human System Interaction - Richmond, United States
Duration: 25 Jun 201927 Jun 2019

Publication series

NameIEEE HSI Proceedings Series
ISSN (Print)2158-2246
ISSN (Electronic)2158-2254


Conference 2019 12th International Conference on Human System Interaction
CountryUnited States


  • Visual Saliency Detection Based on Full CNN_pp

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    Accepted author manuscript (Post-print), 526 KB, PDF document

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