Similarity comparisons for interactive fine-grained categorization
Research output: Contribution to journal › Conference article › Research › peer-review
Current human-in-the-loop fine-grained visual categorization systems depend on a predefined vocabulary of attributes and parts, usually determined by experts. In this work, we move away from that expert-driven and attribute-centric paradigm and present a novel interactive classification system that incorporates computer vision and perceptual similarity metrics in a unified framework. At test time, users are asked to judge relative similarity between a query image and various sets of images, these general queries do not require expert-defined terminology and are applicable to other domains and basic-level categories, enabling a flexible, efficient, and scalable system for fine-grained categorization with humans in the loop. Our system outperforms existing state-of-the-art systems for relevance feedback-based image retrieval as well as interactive classification, resulting in a reduction of up to 43% in the average number of questions needed to correctly classify an image.
Original language | English |
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Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Pages (from-to) | 859-866 |
Number of pages | 8 |
ISSN | 1063-6919 |
DOIs | |
Publication status | Published - 24 Sep 2014 |
Externally published | Yes |
Event | 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States Duration: 23 Jun 2014 → 28 Jun 2014 |
Conference
Conference | 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 |
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Country | United States |
City | Columbus |
Period | 23/06/2014 → 28/06/2014 |
Bibliographical note
Publisher Copyright:
© 2014 IEEE.
ID: 302044146