Color Image Set Recognition Based on Quaternionic Grassmannians

We propose a new method for recognizing color image sets using quaternionic Grassmannians, which use the power of quaternions to capture color information and represent each color image set as a point on the quaternionic Grassmannian. We provide a direct formula to calculate the shortest distance between two points on the quaternionic Grassmannian, and use this distance to build a new classification framework. Experiments on the ETH-80 benchmark dataset show that our method achieves good recognition results. We also discuss some limitations in stability and suggest ways the method can be improved in the future.