PL-VSCN: Patch-level vision similarity compares network for image matching

Image matching plays an important role in various computer vision tasks, such as image retrieval and loop closure detection in Simultaneous Localization and Mapping The authors propose a discriminative patch-based image matching method that converts the problem of whole image matching to that of local patch matching. To construct the patch representation, the Patch-Level Vision Similarity Compare Network (PL-VSCN) is proposed to produce the patch feature. In the image matching process, local patches that potentially contain objects within images are initially detected, and the discriminative feature of each patch is extracted based on the pre-trained PL-VSCN. Then, the similarities between the patch pairs are calculated to construct the similarity matrix, and the corresponding patch pairs are detected based on the mutual matching mechanism on the similarity matrix. Experimental results indicate that the proposed PL-VSCN can generate the discriminative patch feature, which can accurately match the patch pairs with the corresponding content and distinguish those with non-corresponding content. In addition, the comparison experiments demonstrate that the proposed image matching method outperforms existing approaches on most datasets and effectively completes the image matching task.

Automated summary

The proposed method introduces a discriminative patch-based image matching approach, which transforms the problem of whole image matching into local patch matching, and successfully completes the image matching task by constructing a similarity matrix and mutual matching mechanism.