SIFT Flow: Dense Correspondence across Different Scenes

While image registration has been studied in different areas of computer vision, aligning images depicting different scenes remains a challenging problem, closer to recognition than to image matching. Analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its neighbors in a large image collection consisting of a variety of scenes. For a query image, histogram intersection on a bag-of-visual-words representation is used to find the set of nearest neighbors in the database. The SIFT flow algorithm then consists of matching densely sampled SIFT features between the two images, while preserving spatial discontinuities. The use of SIFT features allows robust matching across different scene/object appearances and the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach is able to robustly align complicated scenes...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Ce Liu, Jenny Yuen, Antonio B. Torralba, Josef Sivic, William T. Freeman
• Keywords: Computer Vision, ECCV 2008, Large Image Collection, Large Spatial Distortions, SIFT Features, SIFT Flow Algorithm, Single Static Image