A Novel Plausible Model for Visual Perception
Zhiwei Shi, Zhongzhi Shi, Hong Ping Hu · 2006
Traditionally, how to bridge the gap between the low level visual features and the high level semantic concepts has been a tough task for the researchers. In this paper, we propose a novel plausible model, namely globally connected and locally autonomic Bayesian network (GCLABN), to model the process of visual perception. The new model takes advantage of both the low level visual features, such as colors, textures and shapes, of the target object and the interrelationship between the known objects, and integrates them into a Bayesian framework, which possesses both firm theoretical foundation and wide practical applications. According to our meticulous analysis, in many aspects, the novel model theoretically outperforms the original Bayesian network, which has been successfully applied to many related areas, such as object detection, scene analysis and other similar tasks. Finally, although the GCLABN is designed for the visual perception, it also has great potential to be applied to other areas