Improved saliency detection based on superpixel clustering and saliency propagation
Proceedings of the international conference on Multimedia
Figure-ground image segmentation helps weakly-supervised learning of objects
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
REM: relational entropy-based measure of saliency
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Can saliency map models predict human egocentric visual attention?
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Attention prediction in egocentric video using motion and visual saliency
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Non-local spatial redundancy reduction for bottom-up saliency estimation
Journal of Visual Communication and Image Representation
Towards standardization of metrics for evaluation of artificial visual attention
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
Proceedings of the 20th ACM international conference on Multimedia
Tracking the saliency features in images based on human observation statistics
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Depth matters: influence of depth cues on visual saliency
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Visual saliency detection with center shift
Neurocomputing
Learning saliency-based visual attention: A review
Signal Processing
Fast saliency-aware multi-modality image fusion
Neurocomputing
A critical review of selective attention: an interdisciplinary perspective
Artificial Intelligence Review
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
Saliency based mass detection from screening mammograms
Signal Processing
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Computer vision attention processes assign variable-hypothesized importance to different parts of the visual input and direct the allocation of computational resources. This nonuniform allocation might help accelerate the image analysis process. This paper proposes a new bottom-up attention mechanism. Rather than taking the traditional approach, which tries to model human attention, we propose a validated stochastic model to estimate the probability that an image part is of interest. We refer to this probability as saliency and thus specify saliency in a mathematically well-defined sense. The model quantifies several intuitive observations, such as the greater likelihood of correspondence between visually similar image regions and the likelihood that only a few of interesting objects will be present in the scene. The latter observation, which implies that such objects are (relaxed) global exceptions, replaces the traditional preference for local contrast. The algorithm starts with a rough preattentive segmentation and then uses a graphical model approximation to efficiently reveal which segments are more likely to be of interest. Experiments on natural scenes containing a variety of objects demonstrate the proposed method and show its advantages over previous approaches.