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Active object recognition using hierarchical local-receptive-field-based extreme learning machine

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Abstract

In this paper, we develop a method to actively recognize objects by choosing a sequence of actions for an active camera that helps to discriminate between the objects in a dataset. Hierarchical local-receptive-field-based extreme learning machine architecture is developed to jointly learn the state representation and the reinforcement learning strategy. Experimental validation on the publicly available GERMS dataset shows the effectiveness of the proposed method.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants U1613212, 61673238, 91420302, and 61327809, in part by the National High-Tech Research and Development Plan under Grant 2015AA042306, and in part by the National Science & Technology Pillar Program during the 12th Five-year Plan Period (No.2015BAK12B03).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, State Key Lab. of Intelligent Technology and Systems, TNLIST, Beijing, People’s Republic of China

    Huaping Liu & Fuchun Sun

  2. Department of Electronic Information, Taiyuan University of Technology, Shanxi, P.R., China

    Fengxue Li & Xinying Xu

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  1. Huaping Liu
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  2. Fengxue Li
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  3. Xinying Xu
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  4. Fuchun Sun
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Correspondence to Huaping Liu.

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Liu, H., Li, F., Xu, X. et al. Active object recognition using hierarchical local-receptive-field-based extreme learning machine. Memetic Comp. 10, 233–241 (2018). https://doi.org/10.1007/s12293-017-0229-2

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  • DOI: https://doi.org/10.1007/s12293-017-0229-2

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