Robust Duplicate Detection of 2D and 3D Objects

In this paper, we analyze our graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images so that an explicit and complex 3D object modeling is avoided. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of our approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters in a number of applications. Furthermore, effectiveness of our object duplicate detection algorithm is measured over different object classes. Our method is shown to be robust in detecting the same objects even when images with objects are taken from very different viewpoints or distances.