Matching Local Self-Similarities across Images and Videos

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Matching Local Self-Similarities across Images and Videos
We present an approach for measuring similarity between visual entities (images or videos) based on matching internal self-similarities. What is correlated across images (or across video sequences) is the internal layout of local self-similarities (up to some distortions), even though the patterns generating those local self-similarities are quite different in each of the images/videos. These internal self-similarities are efficiently captured by a compact local "self-similarity descriptor", measured densely throughout the image/video, at multiple scales, while accounting for local and global geometric distortions. This gives rise to matching capabilities of complex visual data, including detection of objects in real cluttered images using only rough hand-sketches, handling textured objects with no clear boundaries, and detecting complex actions in cluttered video data with no prior learning. We compare our measure to commonly used image-based and video-based similarity meas...
Eli Shechtman, Michal Irani
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Eli Shechtman, Michal Irani
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