Comparative Study of Visual Attention Models with Human Eye Gaze in Remote Sensing Images
J. Amudha, D. Radha, A.S. Deepa - · 2015
Computational visual attention model analogous to human eye behaviour has a tremendous need for applications in various fields. Study of visual attention yields information about a person's conscious processing while performing a task. This paper evaluates the behaviour of the bottom-up visual attention models with the eye gaze data set based on various performance metrics. The eye tracking data used for the study measures the gaze fixation points of human beings viewing remote sensing images. The evaluation of the models concludes that the Graph Based Visual saliency model predicts better than the Itti-Koch model across all the performance measures such as Normalized Scanpath Saliency (NSS) score, Area Under the Curve (AUC) score and Linear Correlation Coefficient for the remote sensing images.