A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
| Hi-index | 0.00 |
This paper proposes a computational model of attention-based segmentation in which a sequence of image pyramids of early visual features is computed for a video sequence and a repetition of selective attention and figure-ground segmentation is performed on the sequence for object perception through successive segment development with mergence of concurrent segments. Attention is stochastically selected on a multi-level saliency map that is called a visual attention pyramid and segmentation is performed on Markov random fields which are dynamically formed around foci of attention. A set of segments and their spatial relation are stored in a visual working memory and maintained through the repetitive attention and segmentation process. Performances of the model are evaluated for basic functions of the vision system such as visual pop-out, figure-ground reversal and perceptual organization and also for real-world scenes which contain objects designed to attract attention.