Visual place recognition is an interesting technology that can be used in many domains such as localizing historical photos, autonomous navigation and augmented reality. The main stream of research in that domain was based on the use of local invariant features like SIFT. Little attention was given to region descriptors which can encompass local and global visual appearances. In this paper, we provide an empirical study on a particular visual descriptor: covariance matrices. In order to enhance the discriminative power of the descriptor, multi-block based descriptors are designed and compared. We show further experimental results on matching test images with reference images acquired in dense urban scenes in the streets of the city of Paris. Experiments show that the multi-block based matching algorithms can lead to both high accuracy and scalability.