Evaluating information contributions of bottom-up and top-down processes

This paper presents a method to quantitatively evaluate information contributions of individual bottom-up and top-down computing processes in object recognition. Our objective is to start a discovery on how to schedule bottom-up and top-down processes. (1) We identify two bottom-up processes and one top-down process in hierarchical models, termed α, β and γ channels respectively ; (2) We formulate the three channels under an unified Bayesian framework; (3) We use a blocking control strategy to isolate the three channels to separately train them and individually measure their information contributions in typical recognition tasks; (4) Based on the evaluated results, we integrate the three channels to detect objects with performance improvements obtained. Our experiments are performed in both low-middle level tasks, such as detecting edges/bars and junctions, and high level tasks, such as detecting human faces and cars, together with a group of human study designed to compare computer and human perception.