Ordinal pyramid pooling for rotation invariant object recognition

Abstract

Local feature descriptor plays a fundamental role in many visual tasks, and its rotation invariance is a key issue for many recognition and detection problems. This paper proposes a novel rotation invariant descriptor by ordinal pyramid pooling of local Fourier transform features based on their radial gradient orientations. Since both the low-level feature and pooling strategy are rotation invariant, the obtained descriptor is rotation invariant by nature. Pooling based on orders of gradient orientations is not only invariant to in-plane rotation, but also encodes gradient orientation information into descriptor as well as spatial information to some extent. Moreover, these information is enhanced by the proposed pyramid pooling structure. Therefore, our method is naturally rotation invariant and has strong discriminative ability. Experimental results on the aerial car dataset demonstrate the effectiveness of our descriptor.