Is rotation a nuisance in shape recognition?

Qiuhong Ke, Yi Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

25 Citations (Scopus)


Rotation in closed contour recognition is a puzzling nuisance in most algorithms. In this paper we address three fundamental issues brought by rotation in shapes: 1) is alignment among shapes necessary? If the answer is 'no', 2) how to exploit information in different rotations? and 3) how to use rotation unaware local features for rotation aware shape recognition? We argue that the origin of these issues is the use of hand crafted rotation-unfriendly features and measurements. Therefore our goal is to learn a set of hierarchical features that describe all rotated versions of a shape as a class, with the capability of distinguishing different such classes. We propose to rotate shapes as many times as possible as training samples, and learn the hierarchical feature representation by effectively adopting a convolutional neural network. We further show that our method is very efficient because the network responses of all possible shifted versions of the same shape can be computed effectively by re-using information in the overlapping areas. We tested the algorithm on three real datasets: Swedish Leaves dataset, ETH-80 Shape, and a subset of the recently collected Leafsnap dataset. Our approach used the curvature scale space and outperformed the state of the art.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781479951178
Publication statusPublished - 2014
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2014 - Columbus, United States of America
Duration: 23 Jun 201428 Jun 2014 (IEEE Conference Proceedings)


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2014
Abbreviated titleCVPR 2014
Country/TerritoryUnited States of America
Internet address


  • convolutional neural network
  • rotation friendly features
  • Shape recognition

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