Comparing state-of-the-art visual features on invariant object recognition tasks
Name
Pinto et al_IEEE2011_WACV.pdf
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1.62 MB
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Author(s)
Pinto, Nicolas
Barhomi, Youssef
Cox, David D.
DiCarlo, James
Date Issued
January 2011
Journal
Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Pinto, Nicolas et al. “Comparing State-of-the-art Visual Features on Invariant Object Recognition Tasks.” Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV), 5-7 Jan. 2011, Kona, HI, USA, IEEE, 2011. 463–470. Web.
Version
Author's final manuscript
Abstract
Tolerance (“invariance”) to identity-preserving image variation (e.g. variation in position, scale, pose, illumination) is a fundamental problem that any visual object recognition system, biological or engineered, must solve. While standard natural image database benchmarks are useful for guiding progress in computer vision, they can fail to probe the ability of a recognition system to solve the invariance problem. Thus, to understand which computational approaches are making progress on solving the invariance problem, we compared and contrasted a variety of state-of-the-art visual representations using synthetic recognition tasks designed to systematically probe invariance. We successfully re-implemented a variety of state-of-the-art visual representations and confirmed their published performance on a natural image benchmark. We here report that most of these representations perform poorly on invariant recognition, but that one representation shows significant performance gains over two baseline representations. We also show how this approach can more deeply illuminate the strengths and weaknesses of different visual representations and thus guide progress on invariant object recognition.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1109/WACV.2011.5711540
