We propose using the proximity distribution of vector-quantized local feature descriptors for object and category recognition. To this end, we introduce a novel ``proximity distribution kernel'' that naturally combines local geometric as well as photometric information from images. It satisfies Mercer's condition and can therefore be readily combined with a support vector machine to perform visual categorization in a way that is insensitive to photometric and geometric variations, while retaining significant discriminative power. In particular, it improves on the results obtained both with geometrically unconstrained ``bags of features'' approaches, as well as with over-constrained ``affine procrustes.'' Indeed, we test this approach on several challenging data sets, including Graz-01, Graz-02, and the PASCAL challenge and significant improvements are observed over previous reported results.

I may also talk about our empirical study of face recognition across aging progress if time permits.