Visual Manifold Sensing

Irina Burciu, Adrian Ion-Margineanu, Thomas Martinetz, Erhardt Barth


We present a novel method, Manifold Sensing, for the adaptive sampling of the visual world based on manifolds of increasing but low dimensionality that have been learned with representative data. Because the data set is adapted during sampling, every new measurement (sample) depends on the previously acquired measurements. This leads to an efficient sampling strategy that requires a low total number of measurements. We apply Manifold Sensing to object recognition on UMIST, Robotics Laboratory, and ALOI benchmarks. For face recognition, with only 30 measurements - this corresponds to a compression ratio greater than 2000 - an unknown face can be localized such that its nearest neighbor in the low-dimensional manifold is almost always the actual nearest image. Moreover, the recognition rate obtained by assigning the class of the nearest neighbor is 100%. For a different benchmark with everyday objects, with only 38 measurements - in this case a compression ratio greater than 700 - we obtain similar localization results and, again, a 100% recognition rate.

Original languageEnglish
Title of host publicationHuman Vision and Electronic Imaging XIX
EditorsBernice E. Rogowitz, Thrasyvoulos N. Pappas, Huib de Ridder
Number of pages8
Publication date25.02.2014
Pages9014 - 9014 - 8
ISBN (Print)9780819499318
Publication statusPublished - 25.02.2014
EventHuman Vision and Electronic Imaging 2014 - San Francisco, California, United States
Duration: 03.02.201406.02.2014


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