Similarity comparisons for interactive fine-grained categorization

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

Similarity comparisons for interactive fine-grained categorization. / Wah, Catherine; Horn, Grant Van; Branson, Steve; Maji, Subhransu; Perona, Pietro; Belongie, Serge.

In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 24.09.2014, p. 859-866.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Wah, C, Horn, GV, Branson, S, Maji, S, Perona, P & Belongie, S 2014, 'Similarity comparisons for interactive fine-grained categorization', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 859-866. https://doi.org/10.1109/CVPR.2014.115

APA

Wah, C., Horn, G. V., Branson, S., Maji, S., Perona, P., & Belongie, S. (2014). Similarity comparisons for interactive fine-grained categorization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 859-866. https://doi.org/10.1109/CVPR.2014.115

Vancouver

Wah C, Horn GV, Branson S, Maji S, Perona P, Belongie S. Similarity comparisons for interactive fine-grained categorization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014 Sep 24;859-866. https://doi.org/10.1109/CVPR.2014.115

Author

Wah, Catherine ; Horn, Grant Van ; Branson, Steve ; Maji, Subhransu ; Perona, Pietro ; Belongie, Serge. / Similarity comparisons for interactive fine-grained categorization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014 ; pp. 859-866.

Bibtex

@inproceedings{2933679faba046bc8050567e23db0982,
title = "Similarity comparisons for interactive fine-grained categorization",
abstract = "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.",
author = "Catherine Wah and Horn, {Grant Van} and Steve Branson and Subhransu Maji and Pietro Perona and Serge Belongie",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 ; Conference date: 23-06-2014 Through 28-06-2014",
year = "2014",
month = sep,
day = "24",
doi = "10.1109/CVPR.2014.115",
language = "English",
pages = "859--866",
journal = "I E E E Conference on Computer Vision and Pattern Recognition. Proceedings",
issn = "1063-6919",
publisher = "Institute of Electrical and Electronics Engineers",

}

RIS

TY - GEN

T1 - Similarity comparisons for interactive fine-grained categorization

AU - Wah, Catherine

AU - Horn, Grant Van

AU - Branson, Steve

AU - Maji, Subhransu

AU - Perona, Pietro

AU - Belongie, Serge

N1 - Publisher Copyright: © 2014 IEEE.

PY - 2014/9/24

Y1 - 2014/9/24

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84911368243&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2014.115

DO - 10.1109/CVPR.2014.115

M3 - Conference article

AN - SCOPUS:84911368243

SP - 859

EP - 866

JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

SN - 1063-6919

T2 - 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014

Y2 - 23 June 2014 through 28 June 2014

ER -

ID: 302044146