Fast Feature-Oriented Visual Connection for Large Image Collections

Abstract

Deriving the visual connectivity across large image collections is a computationally expensive task. Different from current image-oriented match graph construction methods which build on pairwise image matching, we present a novel and scalable feature-oriented image matching algorithm for large collections. Our method improves the match graph construction procedure in three ways. First, instead of building trees repeatedly, we put the feature points of the input image collection into a single kd-tree and select the leaves as our anchor points. Then we construct an anchor graph from which each feature can intelligently find a small portion of related candidates to match. Finally, we design a new form of adjacency matrix for fast feature similarity measuring, and return all the matches in different photos across the whole dataset directly. Experiments show that our feature-oriented correspondence algorithm can explore visual connectivity between images with significant improvement in speed.