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Graph Matching for Object Detection from RGB-D Images

We propose an optimization method for estimating parameters in graph-theoretical formulations of the matching problem for object detection. Unlike several methods which optimize parameters for graph matching in a way to promote correct correspondences and to restrict wrong ones, our approach aims at improving performance in the more general task of object detection. In our formulation, similarity functions are adjusted so as to increase the overall similarity among a reference model and the observed target, and at the same time reduce the similarity among reference and “non-target” objects. We evaluate the proposed method in a challenging scenario, namely object detection using data captured with a Kinect sensor in a real environment, demonstrating substantial improvements.