© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ ; We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier.; We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds. (C) 2016 Elsevier Inc. All rights reserved. ; Peer Reviewed ; Postprint (author's final draft)


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    Interactive multiple object learning with scanty human supervision

    Villamizar, Michael / Garrell, Anaís / Sanfeliu, Alberto et al. | BASE | 2016

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    Interactive multiple object learning with scanty human supervision

    Villamizar, Michael / Garrell, Anaís / Sanfeliu, Alberto et al. | British Library Online Contents | 2016


    Interactive multiple object learning with scanty human supervision

    Villamizar, Michael / Garrell, Anaís / Sanfeliu, Alberto et al. | British Library Online Contents | 2016


    Interactive multiple object learning with scanty human supervision

    Villamizar, Michael / Garrell, Anaís / Sanfeliu, Alberto et al. | British Library Online Contents | 2016


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    Grewe, L. / Kak, A. / IEEE Computer Society; Technical Committee on Pattern Analysis and Machine Intelligence | British Library Conference Proceedings | 1994