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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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References

  1. Chen S, Li Y, Kwok NM (2008) Active vision in robotic systems: a survey of recent developments. Int J Robot Res 30:1343–1377

    Article  Google Scholar 

  2. Jia Z, jen Chang Y, Chen T (2009) Active view selection for object and pose recognition. In: ICCVW, pp 641–648

  3. Nakath D, Kluth T, Reineking T, Zetzsche C, Schill K (2014) Active sensorimotor object recognition in three-dimensional space. Spat Cogn IX:312–324

    Google Scholar 

  4. Andreopoulos A, Tsotsos JK (2013) A computational learning theory of active object recognition under uncertainty. Int J Comput Vis 10:95–142

    Article  MathSciNet  Google Scholar 

  5. Browatzki B, Tikhanoff V, Metta G, Bulthoff HH, Wallraven C (2014) Active in-hand object recognition on a humanoid robot. IEEE Trans Robot 30:1260–1269

    Article  Google Scholar 

  6. Wu K, Ranasinghe R, Dissanayake G (2015)Active recognition and pose estimation of household objects in clutter. In: ICRA, pp 4230–4237

  7. Potthast C, Breitenmoser A, Sha F, Sukhatme GS (2016) Active multi-view object recognition: a unifying view on online feature selection and view planning. Robot Auton Syst 84:31–47

    Article  Google Scholar 

  8. Imperolia M, Pretto A (2016) Active detection and localization of textureless objects in cluttered environments. In: CVIU, pp 1–18

  9. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518:529–533

    Article  Google Scholar 

  10. Malmir M, Sikka K, Forster D, Movellan J, Cottrell G (2015) Deep q-learning for active recognition of germs: baseline performance on a standardized dataset for active learning. In: BMVC, pp 161–171

  11. Caicedo JC, Lazebnik S (2015) Active object localization with deep reinforcement learning. In: ICCV, pp 1–8

  12. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  13. Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern 42:513–529

    Article  Google Scholar 

  14. Cao J, Lin Z (2015) Extreme learning machines on high dimensional and large data applications: a survey. Math Probl Eng 2015:1–13. doi:10.1155/2015/103796

  15. Cao J, Lin Z, Huang G-B, Liu N (2012) Voting based extreme learning machine. Inf Sci 185(1):66–77

    Article  MathSciNet  Google Scholar 

  16. Lu H, Du B, Liu J, Xia H, Yeap WK (2016) A kernel extreme learning machine algorithm based on improved particle swam optimization. Memet Comput 1–8

  17. Li X, Mao W, Jiang W, Yao Y (2016) Extreme learning machine via free sparse transfer representation optimization. Memet Comput 8(2):85–95

    Article  Google Scholar 

  18. Zhang H, Zhang S, Yin Y Kernel online sequential elm algorithm with sliding window subject to time-varying environments. Memet Comput 1–10

  19. Zhang N, Ding S (2016) Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data. Memet Comput 1–11

  20. Cao J, Zhao Y, Lai X, Ong MEH, Yin C, Koh ZX, Liu N (2015) Landmark recognition with sparse representation classification and extreme learning machine. J Frankl Inst 352(10):4528–4545

    Article  MathSciNet  Google Scholar 

  21. Kan EM, Lim MH, Ong YS, Tan AH, Yeo SP (2013) Extreme learning machine terrain-based navigation for unmanned aerial vehicles. Neural Comput Appl 22(3–4):469–477

    Article  Google Scholar 

  22. Xiao C, Dong Z, Xu Y, Meng K, Zhou X, Zhang X (2016) Rational and self-adaptive evolutionary extreme learning machine for electricity price forecast. Memet Comput 8(3):223–233

    Article  Google Scholar 

  23. Cao J, Wang W, Wang J, Wang R (2016) Excavation equipment recognition based on novel acoustic statistical features. IEEE Trans Cybern. doi:10.1109/TCYB.2016.2609999

  24. Das SP, Padhy S (2016) Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index. Memet Comput 1–14

  25. Pan J, Wang X, Cheng Y, Cao G (2012) Reinforcement learning based on extreme learning machine. In: International conference on intelligent computing. Springer, New York, pp 80–86

  26. Lopez-Guede JM, Fernandez-Gauna B, Grana M (2013) State-action value function modeled by elm in reinforcement learning for hose control problems. Int J Uncertain Fuzziness Knowl Based Syst 21(supp02):99–116

    Article  MathSciNet  Google Scholar 

  27. Lopez-Guede JM, Fernandez-Gauna B, Ramos-Hernanz JA (2015) A L-MCRS dynamics approximation by ELM for reinforcement learning. Neurocomputing 150:116–123

    Article  Google Scholar 

  28. Hwangbo J, Gehring C, Bellicoso D, Fankhauser P, Siegwart R, Hutter M (2015) Direct state-to-action mapping for high dof robots using ELM. In: IROS

  29. Malmir M, Sikka K, Forster D, Movellan JR, Cottrell G (2015) Deep Q-learning for active recognition of GERMS: baseline performance on a standardized dataset for active learning. In: BMVC, pp 161–171

  30. Sutton RS, Barto AG (1998) Introduction to reinforcement learning, vol 135. MIT Press, Cambridge

    MATH  Google Scholar 

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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

Authors
  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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