Full ranking as local descriptor for visual recognition: A comparison of distance metrics on sn

Abstract

In this paper we propose to use the full ranking of a set of pixels as a local descriptor. In contrast to existing methods which use only partial ranking information, the full ranking encodes the complete comparative information among the pixels, while retaining invariance to monotonic photometric transformations. The descriptor is used within the bag-of-visual-words paradigm for visual recognition. We demonstrate that the choice of distance metric for assigning the descriptors to visual words is crucial to the performance, and provide an extensive evaluation of eight distance metrics for the permutation group Sn on four widely used face verification and texture classification benchmarks. The results demonstrate that (1) full ranking of pixels encodes more information than partial ranking, consistently leading to better performance; (2) full ranking descriptor can be trivially made rotation invariant; (3) the proposed descriptor applies to both image intensities and filter responses, and is capable of producing state-of-the-art performance.