Matching Local Invariant Features with Contextual Information: An Experimental Evaluation

ELCVIA Electronic Letters on Computer Vision and Image Analysis

Desire Sidibe¹  Philippe Montesinos¹  Stefan Janaqi¹ 

关键词: Image matching;    Local invariant features;    SIFT;    Contextual information;    Object recognition;   

DOI  :  

学科分类：计算机科学（综合）

来源: ELCVIA

【 摘 要 】

The main advantage of using local invariant features is their local character which yields robustness to occlusion and varying background. Therefore, local features have proved to be a powerful tool for finding correspondences between images, and have been employed in many applications. However, the local character limits the descriptive capability of features descriptors, and local features fail to resolve ambiguities that can occur when an image shows multiple similar regions. Considering some global information will clearly help to achieve better performances. The question is which information to use and how to use it. Context can be used to enrich the description of the features, or used in the matching step to filter out mismatches. In this paper, we compare different recent methods which use context for matching and show that better results are obtained if contextual information is used during the matching process. We evaluate the methods in two applications: wide baseline matching and object recognition, and it appears that a relaxation based approach gives the best results.

【 授权许可】

Unknown