Grouping and organizing unordered images for multi-view feature correspondences

Handling numerous unordered images for scene reconstruction and categorization attracts increasing interests for commercial and scientific efforts. In this paper, we address the issue of efficient organization of content-related images from plenty of input images on several scenes with contaminated ones. First a robust view-similarity measure is proposed and the images can be categorized effectively without any constraints; then two speedup strategies, seed growing based grouping and tentative feature matching, are presented respectively. The experimental results on two image dataset demonstrate that the proposed method can efficiently and effectively organize unordered views without any geometric constraints, and can further provide nice data for 3D modeling.