Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models
Attention in Cognitive Systems
A simple method for detecting salient regions
Pattern Recognition
Visual search in static and dynamic scenes using fine-grain top-down visual attention
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Early clustering approach towards modeling of bottom-up visual attention
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Early top-down influences in control of attention: evidence from the attentional blink
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
IEEE Transactions on Image Processing
Video image assessment with a distortion-weighing spatiotemporal visual attention model
Multimedia Tools and Applications
A color saliency model for salient objects detection in natural scenes
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Towards standardization of metrics for evaluation of artificial visual attention
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Visual saliency detection with center shift
Neurocomputing
How do warm colors affect visual attention?
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
How to measure the relevance of a retargeting approach?
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Journal of Visual Communication and Image Representation
Visual Saliency with Statistical Priors
International Journal of Computer Vision
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Visual attention is one of the important phenomena in biological vision which can be followed to achieve more efficiency, intelligence, and robustness in artificial vision systems. This paper investigates a region-based approach that performs pixel clustering prior to the processes of attention in contrast to late clustering as done by contemporary methods. The foundation steps of feature map construction for the region-based attention model are proposed here. The color contrast map is generated based upon the extended findings from the color theory, the symmetry map is constructed using a novel scanning-based method, and a new algorithm is proposed to compute a size contrast map as a formal feature channel. Eccentricity and orientation are computed using the moments of obtained regions and then saliency is evaluated using the rarity criteria. The efficient design of the proposed algorithms allows incorporating five feature channels while maintaining a processing rate of multiple frames per second. Another salient advantage over the existing techniques is the reusability of the salient regions in the high-level machine vision procedures due to preservation of their shapes and precise locations. The results indicate that the proposed model has the potential to efficiently integrate the phenomenon of attention into the main stream of machine vision and systems with restricted computing resources such as mobile robots can benefit from its advantages.