Color Image Set Recognition Based on Quaternionic Grassmannians
This addresses color image set recognition for computer vision applications, but it appears incremental as it builds on existing quaternionic and Grassmannian methods.
The paper tackles the problem of recognizing color image sets by representing them as points on quaternionic Grassmannians and using a direct formula for distance calculation to build a classification framework, achieving good recognition results on the ETH-80 and Highway Traffic datasets.
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 and and the Highway Traffic video 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.