Learning and applying model-based visual context

Abstract

I believe that context's ability to reduce the ambiguity of an input signal makes it a vital constraint for understanding the real world. I specifically examine the role of context in vision and how a model-based approach can aid visual search and recognition. Through the implementation of a system capable of learning visual context models from an image database, I demonstrate the utility of the model-based approach. The system is capable of learning models for "water-horizon scenes" and "suburban street scenes" from a database of 745 images.