Object Identification in Complex Scenes using Shape Context Descriptors and Multi-Stage Clustering

This paper introduces a novel technique for the automatic identification of real world objects in complex scenes. The identification problem requires the comparison of assemblies of image regions with a previously stored view of a known prototype. Shape context representation and matching are employed for recovering point correspondences between the image and the prototype object. This work is inspired from the problem that, a number of ambient conditions such as partial object occlusion and contour distortion, may affect the performance of the matching process and consequently the identification result. In our approach, subtractive clustering is applied in a novel fashion to enable the identification of regions of interest on the complex scene, based on a density metric. In order to increase the robustness of the identifier to mismatches and reduce the computational cost of the process, a selection of the initial suspicious regions is performed. The performance of the identifier has been examined in a great range of image and prototype selections.