Adaboost for concrete type of keywords annotation

Wei Chao Lin; Yan Ze Chen; Shu Yuan Chen

Adaboost for concrete type of keywords annotation

Wei Chao Lin^*, Yan Ze Chen, Shu Yuan Chen

^*此作品的通信作者

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

摘要

The semantic gap presents an arduous task in semantic-based image retrieval investigations. In this paper, the author proposes the AdaBoost learning algorithm for large vocabulary classification. The main finding of this investigation is that using Gentle AdaBoost for image classification produced excellent results in terms of precision and the F-measure. With 190 concrete keywords categorisation, AdaBoost renders more keywords assignable and allows a significant improvement in all accuracy measures: precision, recall and F-measure. An AdaBoost vs. SVMs comparison showed that AdaBoost was an effective classifier using the one-versus-the-rest mode of operation.

原文	英語
主出版物標題	Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018
編輯	Craig Douglas, Korsunsky Korsunsky, Oscar Castillo, S. I. Ao, David Dagan Feng
發行者	Newswood Limited
頁面	293-298
頁數	6
ISBN（電子）	9789881404787
出版狀態	已出版 - 2018
對外發佈	是
事件	2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 - Hong Kong, 香港持續時間: 14 03 2018 → 16 03 2018

出版系列

名字	Lecture Notes in Engineering and Computer Science
卷	2233
ISSN（列印）	2078-0958

Conference

Conference	2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018
國家/地區	香港
城市	Hong Kong
期間	14/03/18 → 16/03/18

文獻附註

Publisher Copyright:
© 2018 Newswood Limited. All rights reserved.

其它文件與鏈接

Link to publication in Scopus

引用此

Lin, W. C., Chen, Y. Z., & Chen, S. Y. (2018). Adaboost for concrete type of keywords annotation. 於 C. Douglas, K. Korsunsky, O. Castillo, S. I. Ao, & D. D. Feng (編輯), Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018 (頁 293-298). (Lecture Notes in Engineering and Computer Science; 卷 2233). Newswood Limited.

Lin, Wei Chao ; Chen, Yan Ze ; Chen, Shu Yuan. / Adaboost for concrete type of keywords annotation. Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. 編輯 / Craig Douglas ; Korsunsky Korsunsky ; Oscar Castillo ; S. I. Ao ; David Dagan Feng. Newswood Limited, 2018. 頁 293-298 (Lecture Notes in Engineering and Computer Science).

@inproceedings{7d6730d1d1764d07a3e7e0b75bc18481,

title = "Adaboost for concrete type of keywords annotation",

abstract = "The semantic gap presents an arduous task in semantic-based image retrieval investigations. In this paper, the author proposes the AdaBoost learning algorithm for large vocabulary classification. The main finding of this investigation is that using Gentle AdaBoost for image classification produced excellent results in terms of precision and the F-measure. With 190 concrete keywords categorisation, AdaBoost renders more keywords assignable and allows a significant improvement in all accuracy measures: precision, recall and F-measure. An AdaBoost vs. SVMs comparison showed that AdaBoost was an effective classifier using the one-versus-the-rest mode of operation.",

keywords = "Image annotation 、 Content-based image retrieval、AdaBoost learning algorithm",

author = "Lin, {Wei Chao} and Chen, {Yan Ze} and Chen, {Shu Yuan}",

note = "Publisher Copyright: {\textcopyright} 2018 Newswood Limited. All rights reserved.; 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 ; Conference date: 14-03-2018 Through 16-03-2018",

year = "2018",

language = "英语",

series = "Lecture Notes in Engineering and Computer Science",

publisher = "Newswood Limited",

pages = "293--298",

editor = "Craig Douglas and Korsunsky Korsunsky and Oscar Castillo and Ao, {S. I.} and Feng, {David Dagan}",

booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018",

}

Lin, WC, Chen, YZ & Chen, SY 2018, Adaboost for concrete type of keywords annotation. 於 C Douglas, K Korsunsky, O Castillo, SI Ao & DD Feng (編輯), Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. Lecture Notes in Engineering and Computer Science, 卷 2233, Newswood Limited, 頁 293-298, 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018, Hong Kong, 香港, 14/03/18.

Adaboost for concrete type of keywords annotation. / Lin, Wei Chao; Chen, Yan Ze; Chen, Shu Yuan.
Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. 編輯 / Craig Douglas; Korsunsky Korsunsky; Oscar Castillo; S. I. Ao; David Dagan Feng. Newswood Limited, 2018. p. 293-298 (Lecture Notes in Engineering and Computer Science; 卷 2233).

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

TY - GEN

T1 - Adaboost for concrete type of keywords annotation

AU - Lin, Wei Chao

AU - Chen, Yan Ze

AU - Chen, Shu Yuan

PY - 2018

Y1 - 2018

N2 - The semantic gap presents an arduous task in semantic-based image retrieval investigations. In this paper, the author proposes the AdaBoost learning algorithm for large vocabulary classification. The main finding of this investigation is that using Gentle AdaBoost for image classification produced excellent results in terms of precision and the F-measure. With 190 concrete keywords categorisation, AdaBoost renders more keywords assignable and allows a significant improvement in all accuracy measures: precision, recall and F-measure. An AdaBoost vs. SVMs comparison showed that AdaBoost was an effective classifier using the one-versus-the-rest mode of operation.

AB - The semantic gap presents an arduous task in semantic-based image retrieval investigations. In this paper, the author proposes the AdaBoost learning algorithm for large vocabulary classification. The main finding of this investigation is that using Gentle AdaBoost for image classification produced excellent results in terms of precision and the F-measure. With 190 concrete keywords categorisation, AdaBoost renders more keywords assignable and allows a significant improvement in all accuracy measures: precision, recall and F-measure. An AdaBoost vs. SVMs comparison showed that AdaBoost was an effective classifier using the one-versus-the-rest mode of operation.

KW - Image annotation 、 Content-based image retrieval、AdaBoost learning algorithm

UR - http://www.scopus.com/inward/record.url?scp=85058700427&partnerID=8YFLogxK

M3 - 会议稿件

AN - SCOPUS:85058700427

T3 - Lecture Notes in Engineering and Computer Science

SP - 293

EP - 298

BT - Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018

A2 - Douglas, Craig

A2 - Korsunsky, Korsunsky

A2 - Castillo, Oscar

A2 - Ao, S. I.

A2 - Feng, David Dagan

PB - Newswood Limited

T2 - 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018

Y2 - 14 March 2018 through 16 March 2018

ER -

Adaboost for concrete type of keywords annotation

摘要

出版系列

Conference

文獻附註

其它文件與鏈接

指紋

引用此