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The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Matching Local Self-Similarities across Images and Videos

Shechtman, Eli, Irani, Michal·2007·2007 IEEE Conference on Computer Vision and Pattern Recognition

Abstract

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 measures, and demonstrate its applicability to object detection, retrieval, and action detection.

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Metadata

Authors
Shechtman, E., & Irani, M.
Venue
2007 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2007-06
Pages
1-8
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2144502914
Cited by
1,076
References
27
FWCI
32.57
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Matching Local Self-Similarities across Images and Videos. https://app.cochat.ai/papers/10.1109/cvpr.2007.383198
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Shechtman, E., & Irani, M. (2007). Matching Local Self-Similarities across Images and Videos. 2007 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2007.383198

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