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Selecting a Small Set of Optimal Gestures from an Extensive Lexicon

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 119 - Number 5
Year of Publication: 2015
Authors:
Jacob Grosek
J. Nathan Kutz
10.5120/21060-3722

Jacob Grosek and Nathan J Kutz. Article: Selecting a Small Set of Optimal Gestures from an Extensive Lexicon. International Journal of Computer Applications 119(5):1-8, June 2015. Full text available. BibTeX

@article{key:article,
	author = {Jacob Grosek and J. Nathan Kutz},
	title = {Article: Selecting a Small Set of Optimal Gestures from an Extensive Lexicon},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {119},
	number = {5},
	pages = {1-8},
	month = {June},
	note = {Full text available}
}

Abstract

Finding the best set of gestures to use for a given computer recognition problem is an essential part of optimizing the recognition performance while being mindful to those who may articulate the gestures. An objective function, called the ellipsoidal distance ratio metric (EDRM), for determining the best gestures from a larger lexicon library is presented, along with a numerical method for incorporating subjective preferences. In particular, we demonstrate an efficient algorithm that chooses the best n gestures from a lexicon of m gestures where typically n ! m using a weighting of both subjective and objective measures.

References

  • R. Cipolla, A. Pentland, Computer Vision for Human- Machine Interaction, Cambridge University Press, 1998.
  • V. Pavlovic, R. Sharma, T. Huang, Visual interpretation of hand gestures for human-computer interaction: A review, IEEE Trans. Patt. Anal. Machine Intell. 19 (1997) 677–695.
  • X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. McLachlan, A. Ng, B. Liu, P. Yu, Z. Zhou, M. Steinbach, D. Hand, D. Steinberg, Top 10 algorithms in data mining, Knowledge and Information Systems 14 (1) (2008) 1–37.
  • F. J. , L. J. , A Selective Overview of Variable Selection in High Dimensional Feature Space, Stat. Sinica 20 (2010) 101–148.
  • D. Donoho, High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality, in: Math Challenges of the 21st Century, Stanford University, Los Angeles, CA, 2000.
  • H. Birk, T. Moeslund, C. Madsen, Real-Time Recognition of Hand Alphabet Gestures Using Principal Component Analysis, in: 10th Scandinavian Conf. Image Analysis, 1997.
  • M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience 3 (1) (1991) 71–86.
  • G. C. Feng, P. C. Yuen, Variance projection function and its application to eye detection for human face recognition, Pattern Recognition Letters 19 (1998) 899–906.
  • Z. Zhou, X. Geng, Projection functions for eye detection, Journal of Pattern Recognition 37 (2004) 1049–1056.
  • S. Mika, G. R atsch, J. Weston, B. Sch olkopf, K. Muller, Fisher Discriminant Analysis with Kernels, in: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, 1999, pp. 41–48.
  • Q. Gu, Z. Li, J. Han, Generalized Fisher Score for Feature Selection, in: Proceedings of the International Conference on Uncertainty in Artificial Intelligence, 2011.
  • D. Koller, M. Sahami, Toward Optimal feature Selection, Technical Report, Standford InfoLab (1996).
  • M. Robnik-¢ Sikonja, I. Kononenko, Theoretical and Empirical Analysis of ReliefF and RReliefF, Machine Learning 53 (1-2) (2003) 23–69.
  • X. He, D. Cai, P. Niyogi, Laplacian Score for Feature Selection, in: Advances in Neural Information Processing Systems 18, MIT Press, Cambridge, MA, 2006, pp. 507–514.
  • L. Song, A. Smola, A. Gretton, K. Borgwardt, J. Bedo, Supervised feature selection via dependence estimation, in: Proc. 24th Intern. Conf. Machine Learning, 2007, pp. 823–830.
  • F. Nie, S. Xiang, Y. Jia, C. Zhang, S. Yan, Trace Ratio Criterion for Feature Selection, in: Proc. 23rd Assoc. Adv. Artificial Intell. Conf. , 2008, pp. 671–676.
  • P. Lisboa, A. Mehri-Dehnavi, Sensitivity Methods for Variable Selection Using the MLP, in: Inte. Work. Neural Net. Ident. Cont. Rob. Signal/Image, 1996, pp. 330–338.
  • Y. Lu, I. Cohen, X. Zhou, Q. Tian, Feature Selection Using Principal Feature Analysis, in: Proceedings of the 15th Annual International Association of Computing Machinery Multimedia Conference, 2007, pp. 301–304.
  • A. Barczak, N. Reyes, M. Abastillas, A. Piccio, T. Susnjak, A New 2D Static Hand Gesture Colour Image Dataset for ASL Gestures, Research Letters in the Information and Mathematical Sciences 15 (2011) 12–20.
  • J. Grosek, P. Shi, J. Kutz, Enhanced Gesture Recognition Performance through Improved Pre-Processing, International Journal of Computer Applications 62 (9) (2013) 1–8.
  • S. Mika, G. R¨atsch, J. Weston, B. Sch¨olkopf. , K. M¨uller, Fisher Discriminant Analysis with Kernels, in: Proc. IEEE Work. Neural Net. Signal Proces. , 1999, pp. 41–48.