Local features for object class recognition

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Abstract
In this paper, we compare the performance of local detectors and descriptors in the context of object class recognition. Recently, many detectors/descriptors have been evaluated in the context of matching as well as invariance to viewpoint changes (Mikolajczyk and Schmid, 2004). However, it is unclear if these results can be generalized to categorization problems, which require different properties of features. We evaluate 5 state-of-the-art scale invariant region detectors and 5 descriptors. Local features are computed for 20 object classes and clustered using hierarchical agglomerative clustering. We measure the quality of appearance clusters and location distributions using entropy as well as precision. We also measure how the clusters generalize from training set to novel test data. Our results indicate that attended SIFT descriptors (Mikolajczyk and Schmid, 2005) computed on Hessian-Laplace regions perform best. Second score is obtained by salient regions (Kadir and Brady, 2001). The results also show that these two detectors provide complementary features. The new detectors/descriptors significantly improve the performance of a state-of-the art recognition approach (Leibe, et al., 2005) in pedestrian detection task

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