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Using Multiple Patches for 3D Object Recognition

Image patches have become increasingly popular in a variety of applications, due to their resistance to clutter and partial occlusion, as well as their partial insensitivity to object pose. Recently Mikolajczyk and Schmid compared a number of local descriptors and concluded that the SIFT-based ones perform best in image matching tasks. In this paper we analyze the performance of three patch descriptors in the context of 3D object recognition: SIFT, PCA-SIFT and keyed context patches. We use a data set containing images of six objects on clean and cluttered backgrounds, taken around the whole viewing sphere, and we look at individual and fused performances. Individually, the keyed context patches perform best overall, but they are outperformed for some objects by SIFT and PCA-SIFT. Recognition is improved by fusing the rankings generated by these classifiers.