HOGgles: Visualizing Object Detection Features
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Torralba_HOGgles.pdf
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10.23 MB
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Adobe PDF
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Author(s)
Vondrick, Carl Martin
Khosla, Aditya
Malisiewicz, Tomasz
Torralba, Antonio
Date Issued
December 2013
Journal
Proceedings of the 2013 IEEE International Conference on Computer Vision
Publisher
IEEE Computer Society
Citation
Vondrick, Carl, Aditya Khosla, Tomasz Malisiewicz, and Antonio Torralba. “HOGgles: Visualizing Object Detection Features.” 2013 IEEE International Conference on Computer Vision, 1-8 Dec. 2013, Sydney, NSW. (December 2013). p.1-8.
Version
Author's final manuscript
Abstract
We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICCV.2013.8
