User Adaptation for Online Sketchy Shape Recognition

Zhengxing Sun; Wenyin Liu; Binbin Peng; Bin Zhang; Jianyong Sun

doi:10.1007/978-3-540-25977-0_28

User Adaptation for Online Sketchy Shape Recognition

Zhengxing Sun
, Wenyin Liu
, Binbin Peng
, Bin Zhang
, Jianyong Sun

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

22 Citations (Scopus)

Abstract

This paper presents a method of online sketchy shape recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user's sketching style in an incremental decision tree, which is then used to recognize the composite shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy shape recognition. © Springer-Verlag 2004.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Verlag
Pages	305-316
Volume	3088
ISBN (Print)	9783540224785
DOIs	https://doi.org/10.1007/978-3-540-25977-0_28
Publication status	Published - 2004
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3088
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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Sun, Z., Liu, W., Peng, B., Zhang, B., & Sun, J. (2004). User Adaptation for Online Sketchy Shape Recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3088, pp. 305-316). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3088). Springer Verlag. https://doi.org/10.1007/978-3-540-25977-0_28

Sun, Zhengxing ; Liu, Wenyin ; Peng, Binbin et al. / User Adaptation for Online Sketchy Shape Recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3088 Springer Verlag, 2004. pp. 305-316 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper presents a method of online sketchy shape recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user's sketching style in an incremental decision tree, which is then used to recognize the composite shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy shape recognition. {\textcopyright} Springer-Verlag 2004.",

author = "Zhengxing Sun and Wenyin Liu and Binbin Peng and Bin Zhang and Jianyong Sun",

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Sun, Z, Liu, W, Peng, B, Zhang, B & Sun, J 2004, User Adaptation for Online Sketchy Shape Recognition. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3088, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3088, Springer Verlag, pp. 305-316. https://doi.org/10.1007/978-3-540-25977-0_28

User Adaptation for Online Sketchy Shape Recognition. / Sun, Zhengxing; Liu, Wenyin; Peng, Binbin et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3088 Springer Verlag, 2004. p. 305-316 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3088).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

TY - CHAP

T1 - User Adaptation for Online Sketchy Shape Recognition

AU - Sun, Zhengxing

AU - Liu, Wenyin

AU - Peng, Binbin

AU - Zhang, Bin

AU - Sun, Jianyong

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2004

Y1 - 2004

N2 - This paper presents a method of online sketchy shape recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user's sketching style in an incremental decision tree, which is then used to recognize the composite shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy shape recognition. © Springer-Verlag 2004.

AB - This paper presents a method of online sketchy shape recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user's sketching style in an incremental decision tree, which is then used to recognize the composite shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy shape recognition. © Springer-Verlag 2004.

UR - https://www.scopus.com/pages/publications/35048832491

UR - https://www.scopus.com/pages/publications/35048832491#tab=impact

U2 - 10.1007/978-3-540-25977-0_28

DO - 10.1007/978-3-540-25977-0_28

M3 - RGC 12 - Chapter in an edited book (Author)

SN - 9783540224785

VL - 3088

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 305

EP - 316

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -

Sun Z, Liu W, Peng B, Zhang B, Sun J. User Adaptation for Online Sketchy Shape Recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3088. Springer Verlag. 2004. p. 305-316. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-25977-0_28

User Adaptation for Online Sketchy Shape Recognition

Abstract

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