High-level and generic models for visual search: When does high level knowledge help?

We analyze the problem of detecting a road target in background clutter and investigate the amount of prior (i.e. target specific) knowledge needed to perform this search task. The problem is formulated in terms of Bayesian inference and we define a Bayesian ensemble of problem instances. This formulation implies that the performance measures of different models depend on order parameters which characterize the problem. This demonstrates that if there is little clutter then only weak knowledge about the target is required in order to detect the target. However at a critical value of the order parameters there is a phase transition and it becomes effectively impossible to detect the target unless high-level target specific knowledge is used. These phase transitions determine different regimes within which different search strategies will be effective. These results have implications for bottom-up and top-down theories of vision.