Abstract
Human gaze is not directed to the same part of an image when lighting conditions change. Current saliency models do not consider light level analysis during their bottom-up processes. In this paper, we introduce a new saliency model which better mimics physiological and psychological processes of our visual attention in case of free-viewing task (bottom-up process). This model analyzes lighting conditions with the aim of giving different weights to color wavelengths. The resulting saliency measure performs better than a lot of popular cognitive approaches.
Highlights
Saliency models are more and more important in computer vision due to their fundamental contribution in intelligent systems
In this paper we propose a bottom-up saliency model which provides a lot of explanations about both biological and Advances in Artificial Intelligence cognitive mechanisms of visual attention
We introduced an improved physiological model of the Features Integration Theory (FIT) by using light level analysis in order to give different weights
Summary
Saliency models are more and more important in computer vision due to their fundamental contribution in intelligent systems (robotics [1], serious games [2], intelligent video surveillance [3], etc.). Object recognition takes place after focusing attention on the where and requires inhibition of feature maps that do not describe the searched target This theory assumes that our attention is deployed sequentially on each stimulus present in the scene. Other approaches are subject to connectionist strategies during their processes of visual attention [10,11,12,13,14] In those models, a neural network describes our visual attention by inhibition and excitation mechanisms that allow the emergence of an area of the scene.
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