"Beyond Covariance: SICE and Kernel Based Visual Feature Representation" by Jianjia Zhang, Lei Wang et al.
Abstract The past several years have witnessed increasing research interest on covariance-based feature representation. Originally proposed as a region descriptor, it has now been used as a general representation in various recognition tasks, demonstrating promising performance. However, covariance matrix has some inherent shortcomings such as singularity in the case of small sample, limited capability in modeling complicated feature relationship, and a single, fixed form of representation. To achieve better recognition performance, this paper argues that more capable and flexible symmetric p...
Source: uow.edu.au