Parameter Estimation in Optimal Object Recognition

Object recognition systems involve parameters such as thresholds, bounds, and weights. These parameters have to be tuned before the system can perform successfully. A common practice is to choose such parameters manually on an ad hoc basis, which is a disadvantage. This chapter1 presents a theory of parameter estimation for optimization-based object recognition where the optimal solution is defined as the global minimum of an energy function. The theory is based on supervised learning from training examples. Correctness and instability are established as criteria for evaluating the estimated parameters. A correct estimate enables the labeling implied in each example con- figuration to be encoded in a unique global energy minimum. The instability is the ease with which the minimum is replaced by a nonexample configuration after a perturbation. The optimal estimate minimizes the instability. Algorithms are presented for computing correct and minimal-instability estimates. The theory is applied to the parameter estimation for MRF-based recognition, and promising results are obtained.