Learning localized perceptual similarity metrics for interactive categorization
Research output: Contribution to journal › Conference article › Research › peer-review
Current similarity-based approaches to interactive fine grained categorization rely on learning metrics from holistic perceptual measurements of similarity between objects or images. However, making a single judgment of similarity at the object level can be a difficult or overwhelming task for the human user to perform. Secondly, a single general metric of similarity may not be able to adequately capture the minute differences that discriminate fine-grained categories. In this work, we propose a novel approach to interactive categorization that leverages multiple perceptual similarity metrics learned from localized and roughly aligned regions across images, reporting state-of-the-art results and outperforming methods that use a single nonlocalized similarity metric.
Original language | English |
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Journal | Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 |
Pages (from-to) | 502-509 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 19 Feb 2015 |
Externally published | Yes |
Event | 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, United States Duration: 5 Jan 2015 → 9 Jan 2015 |
Conference
Conference | 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 |
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Country | United States |
City | Waikoloa |
Period | 05/01/2015 → 09/01/2015 |
Bibliographical note
Publisher Copyright:
© 2015 IEEE.
ID: 301829777