Correction of misclassifications in primary local image recognition using a nonlinear graph-based estimation technique

A description of an approach to primary local image recognition is given. The motivation for its application and its characteristics are discussed. Then a method for correction of misclassifications that occur in primary local image recognition is proposed. This method uses a graph-based estimation technique that uses information contained in supplementary classes in order to remove misclassifications and/or confirm the correct recognition of pixel hypotheses. In addition, the method is able to remove the supplementary classes after they are no longer needed. The particular features of the considered approach are that it is iterative and uses structures similar to those of center weighted median filters. The numerical simulation results are presented to illustrate the efficiency of the proposed technique.