Compound Exemplar Based Object Detection by Incremental Random Forest

This paper describes a new hybrid detection method that combines exemplar based approach with discriminative patch selection. More specifically, we applied a modified random forest for retrieval of input similar local patches of stored exemplars while rejecting background patches. A recursive algorithm based on dynamic programming 2D matching optimization is applied after the aforementioned patch retrieving stage in order to enforce geometric constraints of object patches. Our proposed approach demonstrates experimentally that it performs well while maintaining the capability for incremental learning.