Dynamic Markov random fields for stochastic modeling of visual attention
Akisato Kimura, Derek Pang, Tatsuto Takeuchi, Junji Yamato, Kunio Kashino · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
This report proposes a new stochastic model of visual attention to predict the likelihood of where humans typically focus on a video scene. The proposed model is composed of a dynamic Bayesian network that simulates and combines a person’s visual saliency response and eye movement patterns to estimate the most probable regions of attention. Dynamic Markov random field (MRF) models are newly introduced to include spatiotemporal relationships of visual saliency responses. Experimental results have revealed that the propose model outperforms the previous deterministic model and the stochastic model without dynamic MRF in predicting human visual attention.