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Open AccessArticle

Visual Saliency Prediction Based on Deep Learning

Faculty of Engineering & Applied Science, Memorial University, St. John’s, Newfoundland, NL A1B 3X5, Canada
Faculty of Engineering, Elmergib University, Khoms 40414, Libya
Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus, Kelowna, BC V1V 1V7, Canada
C-CORE, Captain Robert A. Bartlett Building, Morrissey Road, St. John’s, Newfoundland, NL A1C 3X5, Canada
Author to whom correspondence should be addressed.
Information 2019, 10(8), 257;
Received: 25 May 2019 / Revised: 30 July 2019 / Accepted: 8 August 2019 / Published: 12 August 2019
PDF [2221 KB, uploaded 13 August 2019]


Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency. View Full-Text
Keywords: visual saliency; Convolutional Neural Networks; VGG-16; semantic segmentation; deep learning visual saliency; Convolutional Neural Networks; VGG-16; semantic segmentation; deep learning

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Ghariba, B.; Shehata, M.S.; McGuire, P. Visual Saliency Prediction Based on Deep Learning. Information 2019, 10, 257.

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