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 language | English |
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Title of host publication | 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509006410 |
DOIs | |
State | Published - 23 May 2016 |
Event | IEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States Duration: 7 Mar 2016 → 10 Mar 2016 |
Publication series
Name | 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
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Conference
Conference | IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
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Country/Territory | United States |
City | Lake Placid |
Period | 7/03/16 → 10/03/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.