Texture instance similarity via dense correspondences

Tal Hassner, Gilad Saban, Lior Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper concerns the task of evaluating the similarity of textures instances: Rather than discriminating between different texture classes, our goal is to identify when two images display the same texture instance. To address this problem, we propose an approach inspired by alignment based recognition theories. We offer a pixel-based method, employing a robust, dense correspondence estimation engine, applied to an efficient, novel representation, to match the pixels of two texture photos. We describe means for quantifying the quality of these matches, considering in particular the quality of the flow established between the two images. These quality measures are effectively combined into similarity scores by using standard linear SVM classifiers. By relying on a general, alignment based approach our method can be applied to different problem domains (different texture classes) with little modification. We demonstrate this by reporting state-of-the-art results on benchmarks for fingerprint recognition and two new benchmarks for texture-based animal identification.

Original languageEnglish
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - 23 May 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: 7 Mar 201610 Mar 2016

Publication series

Name2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision, WACV 2016
Country/TerritoryUnited States
CityLake Placid
Period7/03/1610/03/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

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