A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attention-Based Segmentation on an Image Pyramid Sequence
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Probabilistic learning of visual object composition from attended segments
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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This paper proposes a new probabilistic model of visual attention, figure-ground segmentation and perceptual organization. In this model, spatially parallel preattentive points on a saliency map are organized into sequential selective attended segments through figure-ground segmentation on dynamically-formed Markov random fields and perceptual organization among attended segments are performed in visual working memory for constructive object recognition. Selective attention to segments is controlled based on their saliency, closedness and attention bias. Attended segments in visual working memory are perceptually organized according to a law of proximity. Experiments were conducted by using images of plural categories in an image database and it was shown that selective attention was frequently turned to objects of those categories and that part segments of objects or salient context of objects were perceptually organized.