文章基本信息

标题：Visual Saliency Prediction Based on Deep Learning
本地全文：下载
作者：Bashir Ghariba ; Mohamed S. Shehata ; Peter McGuire 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2019
卷号：10
期号：8
页码：1-15
DOI：10.3390/info10080257
出版社：MDPI Publishing
摘要：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.
关键词：visual saliency; Convolutional Neural Networks; VGG-16; semantic segmentation; deep learning visual saliency ; Convolutional Neural Networks ; VGG-16 ; semantic segmentation ; deep learning