Abstract
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.
Highlights
Humans have a strong ability to pay attention to a specific part of an image instead of processing the entire image
This study aims to propose the application of a semantic segmentation model based on the VGG-16 network to predict human visual attention in the field of view
We first qualitatively tested the proposed model with the SALICON dataset; we evaluated the model with the TORONTO, MIT300, MIT1003, and DUT-OMRON datasets
Summary
Humans have a strong ability to pay attention to a specific part of an image instead of processing the entire image. This phenomenon of visual attention has been studied for over a century [1]. Top-down approaches are task-oriented and try to locate a target object from a specific category. They depend on the features of the object of interest [4,5]. Bottom-up and top-down approaches are mainly driven by the visual characteristics of a scene and the task of interest, respectively [6,7]
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