A region covariances-based visual attention model for RGB-D images
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2016, Vol 4, Issue 4
Abstract
Existing computational models of visual attention generally employ simple image features such as color, intensity or orientation to generate a saliency map which highlights the image parts that attract human attention. Interestingly, most of these models do not process any depth information and operate only on standard two-dimensional RGB images. On the other hand, depth processing through stereo vision is a key characteristics of the human visual system. In line with this observation, in this study, we propose to extend two state-of-the-art static saliency models that depend on region covariances to process additional depth information available in RGB-D images. We evaluate our proposed models on NUS-3D benchmark dataset by taking into account different evaluation metrics. Our results reveal that using the additional depth information improves the saliency prediction in a statistically significant manner, giving more accurate saliency maps.
Authors and Affiliations
Erkut Erdem*| Hacettepe University. Department of Computer Engineering, Ankara, Turkey – TR-06800
Keywords
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- EP ID EP811
- DOI 10.18201/ijisae.2016426384
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