Object recognition using local descriptors: A comparison

Abstract

Local image descriptors have been widely researched and used, due to their resistance to clutter and partial occlusion, as well as their partial insensitivity to object pose. Recently Mikolajczyk and Schmid [1] compared a number of such descriptors and concluded that the SIFT-based ones perform best in image matching tasks. This paper compares the effect that three local descriptors have on object recognition: SIFT [2], PCA-SIFT [3] and keyed context patches [4]. We use a data set containing images of six objects on clean and cluttered backgrounds, taken around the whole viewing sphere. We conclude that keyed context patches perform best overall, but they are outperformed for some objects by the second best feature, PCA-SIFT. © Springer-Verlag Berlin Heidelberg 2006.