short-paper

A $-Family Friendly Approach to Prototype Selection

Authors:

Eugene M. Taranta II,

Joseph J. LaViola, Jr.Authors Info & Claims

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

Pages 370 - 374

https://doi.org/10.1145/2856767.2856808

Published: 07 March 2016 Publication History

Abstract

We explore the benefits of intelligent prototype selection for $-family recognizers. Currently, the state of the art is to randomly select a subset of prototypes from a dataset without any processing. This results in reduced computation time for the recognizer, but also increases error rates. We propose applying optimization algorithms, specifically random mutation hill climb and a genetic algorithm, to search for reduced sets of prototypes that minimize recognition error. After an evaluation, we found that error rates could be reduced compared to random selection and rapidly approached the baseline accuracies for a number of different $-family recognizers.

References

[1]

Almaksour, A., Anquetil, E., Quiniou, S., and Cheriet, M. Personalizable pen-based interface using lifelong learning. In 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR) (Nov 2010), 188--193.

Digital Library

[2]

Anthony, L., and Wobbrock, J. O. A lightweight multistroke recognizer for user interface prototypes. In Proceedings of Graphics Interface 2010, GI '10, Canadian Information Processing Society (Toronto, Ont., Canada, Canada, 2010), 245--252.

Digital Library

[3]

Anthony, L., and Wobbrock, J. O. $n-protractor: A fast and accurate multistroke recognizer. In Proceedings of Graphics Interface 2012, GI '12, Canadian Information Processing Society (Toronto, Ont., Canada, Canada, 2012), 117--120.

Digital Library

[4]

García, S., Luengo, J., and Herrera, F. Data preprocessing in data mining. Springer, 2015.

Digital Library

[5]

Goldberg, D., and Richardson, C. Touch-typing with a stylus. In Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems, CHI '93, ACM (New York, NY, USA, 1993), 80--87.

Digital Library

[6]

Herold, J., and Stahovich, T. F. The 1-cent: Recognizer: A fast, accurate, and easy-to-implement handwritten gesture recognition technique. In Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling, SBIM '12, Eurographics Association (Aire-la-Ville, Switzerland, Switzerland, 2012), 39--46.

Digital Library

[7]

Leiva, L. A., Martín-Albo, D., and Plamondon, R. Gestures 'A go go: Authoring synthetic human-like stroke gestures using the kinematic theory of rapid movements. ACM Trans. Intell. Syst. Technol. 7, 2 (Nov. 2015), 15:1--15:29.

Digital Library

[8]

Li, Y. Protractor: A fast and accurate gesture recognizer. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '10, ACM (New York, NY, USA, 2010), 2169--2172.

Digital Library

[9]

Llorens, D., Prat, F., Marzal, A., Vilar, J. M., Castro, M. J., Amengual, J.-C., Barrachina, S., Castellanos, A., Boquera, S. E., G´omez, J., et al. The ujipenchars database: a pen-based database of isolated handwritten characters. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, and D. Tapias, Eds., European Language Resources Association (ELRA) (Marrakech, Morocco, may 2008). http://www.lrec-conf.org/proceedings/lrec2008/.

[10]

Mitchell, M., and Holland, J. H. When will a genetic algorithm outperform hill-climbing?

[11]

Park, H.-S., and Jun, C.-H. A simple and fast algorithm for k-medoids clustering. Expert Systems with Applications 36, 2 (2009), 3336--3341.

Digital Library

[12]

Rubine, D. Specifying gestures by example. SIGGRAPH Computer Graphics 25, 4 (July 1991), 329--337.

Digital Library

[13]

Skalak, D. B. Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Machine Learning: Proceedings of the Eleventh International Conference, Morgan Kaufmann (1994), 293--301.

Digital Library

[14]

Taranta, II, E. M., and LaViola, Jr., J. J. Penny pincher: A blazing fast, highly accurate $-family recognizer. In Proceedings of the 41st Graphics Interface Conference, GI '15, Canadian Information Processing Society (Toronto, Ont., Canada, Canada, 2015), 195--202.

Digital Library

[15]

Vatavu, R.-D., Anthony, L., and Wobbrock, J. O. Gestures as point clouds: A p recognizer for user interface prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction, ICMI '12, ACM (New York, NY, USA, 2012), 273--280.

Digital Library

[16]

Vatavu, R.-D., Vogel, D., Casiez, G., and Grisoni, L. Estimating the perceived difficulty of pen gestures. In Proceedings of the 13th IFIP TC 13 International Conference on Human-computer Interaction - Volume Part II, INTERACT'11, Springer-Verlag (Berlin, Heidelberg, 2011), 89--106.

[17]

Wobbrock, J. O., Wilson, A. D., and Li, Y. Gestures without libraries, toolkits or training: A 1 recognizer for user interface prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, UIST '07, ACM (New York, NY, USA, 2007), 159--168.

Digital Library

Cited By

Taranta EMaslych MGhamandi RLaViola J(2022)The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold SelectionProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502000(1-15)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502000
Magrofuoco NRoselli PVanderdonckt J(2021)Two-dimensional Stroke Gesture RecognitionACM Computing Surveys10.1145/346540054:7(1-36)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3465400
Ledda ESpano L(2021)Applying Long-Short Term Memory Recurrent Neural Networks for Real-Time Stroke RecognitionCompanion of the 2021 ACM SIGCHI Symposium on Engineering Interactive Computing Systems10.1145/3459926.3464754(50-55)Online publication date: 8-Jun-2021
https://dl.acm.org/doi/10.1145/3459926.3464754
Show More Cited By

Index Terms

A $-Family Friendly Approach to Prototype Selection
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools

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Information

Published In

cover image ACM Conferences

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

March 2016

446 pages

ISBN:9781450341370

DOI:10.1145/2856767

General Chairs:
Jeffrey Nichols
Google Inc, USA
,
Jalal Mahmud
IBM Research, USA
,
John O'Donovan
UC Santa Barbara, USA
,
Program Chairs:
Cristina Conati
University of British Columbia, Canada
,
Massimo Zancanaro
Bruno Kessler Foundation, Italy

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 March 2016

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IUI'16

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IUI'16: 21st International Conference on Intelligent User Interfaces

March 7 - 10, 2016

California, Sonoma, USA

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IUI '16 Paper Acceptance Rate 49 of 194 submissions, 25%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Taranta EMaslych MGhamandi RLaViola J(2022)The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold SelectionProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502000(1-15)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502000
Magrofuoco NRoselli PVanderdonckt J(2021)Two-dimensional Stroke Gesture RecognitionACM Computing Surveys10.1145/346540054:7(1-36)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3465400
Ledda ESpano L(2021)Applying Long-Short Term Memory Recurrent Neural Networks for Real-Time Stroke RecognitionCompanion of the 2021 ACM SIGCHI Symposium on Engineering Interactive Computing Systems10.1145/3459926.3464754(50-55)Online publication date: 8-Jun-2021
https://dl.acm.org/doi/10.1145/3459926.3464754
Bufano RCostagliola GDe Rosa MFuccella V(2021) PolyRec Gesture Design Tool : A tool for fast prototyping of gesture‐based mobile applications Software: Practice and Experience10.1002/spe.302452:2(594-618)Online publication date: 13-Sep-2021
https://doi.org/10.1002/spe.3024
Vanderdonckt JRoselli PPérez-Medina JD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)!FTL, an Articulation-Invariant Stroke Gesture Recognizer with Controllable Position, Scale, and Rotation InvariancesProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3243032(125-134)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3242969.3243032
Vatavu RAnthony LWobbrock JBailie LOliver N(2018)$QProceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3229434.3229465(1-12)Online publication date: 3-Sep-2018
https://dl.acm.org/doi/10.1145/3229434.3229465
Vanderdonckt JDumas BCherubini MMandryk RHancock MPerry MCox A(2018)Comparing Some Distances in Template-based 2D Gesture RecognitionExtended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3170427.3188452(1-6)Online publication date: 20-Apr-2018
https://dl.acm.org/doi/10.1145/3170427.3188452
Vatavu R(2017)Characterizing gesture knowledge transfer across multiple contexts of useJournal on Multimodal User Interfaces10.1007/s12193-017-0247-x11:4(301-314)Online publication date: 29-Aug-2017
https://doi.org/10.1007/s12193-017-0247-x
Taranta EMaghoumi MPittman CLaViola JRekimoto JIgarashi TWobbrock JAvrahami D(2016)A Rapid Prototyping Approach to Synthetic Data Generation for Improved 2D Gesture RecognitionProceedings of the 29th Annual Symposium on User Interface Software and Technology10.1145/2984511.2984525(873-885)Online publication date: 16-Oct-2016
https://dl.acm.org/doi/10.1145/2984511.2984525

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