Publications-Proceedings

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 Learning collaborative decision-making parameters for multimodal emotion recognition
作者 Huang, K.-C.;Lin, H.-Y.S.;Chan, J.-C.;Kuo, Yau-Hwang
郭耀煌
貢獻者 資科系
關鍵詞 Affective communication; Classification methods; Collaborative decision making; Emotion recognition; Genetic learning; Local minima problems; Multimodal emotion recognition; Optimal solutions; Exhibitions; Decision making
日期 2013-07
上傳時間 26-May-2015 18:28:14 (UTC+8)
摘要 In this paper, we present a novel multimodal emotion recognition technique that automatically learns decision-making parameters customized for each modality. Specifically, the process of decision-making is implemented in a multi-stage and collaborative fashion: Given a classifier for single modality, the classifier is regarded as a virtual expert since classification methods can make emotion recognition in accordance with certain expertise. Then, in the reputation equalization, the expert`s classification capability is then quantitatively equalized to assure the reputation and/or confidence for each expert. To compromise decisions among experts, the final decision is obtained by calculating the weighted-sum of all the equalized reputation quantities, in such a way that the decision of one expert can be made in collaboration with that of the others. Moreover, to learn the proposed model parameters, the genetic algorithm is tailored and applied to alleviate the local minima problem during the process of finding an optimal solution. The experimental results have shown that the proposed collaborative decision-making model is effective in multimodal emotion recognition. © 2013 IEEE.
關聯 Proceedings - IEEE International Conference on Multimedia and Expo, 2013, 論文編號 6607472, 2013 IEEE International Conference on Multimedia and Expo, ICME 2013; San Jose, CA; United States; 15 July 2013 到 19 July 2013; 類別編號CFP13ICM-ART; 代碼 100169
資料類型 conference
DOI http://dx.doi.org/10.1109/ICME.2013.6607472
dc.contributor 資科系
dc.creator (作者) Huang, K.-C.;Lin, H.-Y.S.;Chan, J.-C.;Kuo, Yau-Hwang
dc.creator (作者) 郭耀煌zh_TW
dc.date (日期) 2013-07
dc.date.accessioned 26-May-2015 18:28:14 (UTC+8)-
dc.date.available 26-May-2015 18:28:14 (UTC+8)-
dc.date.issued (上傳時間) 26-May-2015 18:28:14 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75326-
dc.description.abstract (摘要) In this paper, we present a novel multimodal emotion recognition technique that automatically learns decision-making parameters customized for each modality. Specifically, the process of decision-making is implemented in a multi-stage and collaborative fashion: Given a classifier for single modality, the classifier is regarded as a virtual expert since classification methods can make emotion recognition in accordance with certain expertise. Then, in the reputation equalization, the expert`s classification capability is then quantitatively equalized to assure the reputation and/or confidence for each expert. To compromise decisions among experts, the final decision is obtained by calculating the weighted-sum of all the equalized reputation quantities, in such a way that the decision of one expert can be made in collaboration with that of the others. Moreover, to learn the proposed model parameters, the genetic algorithm is tailored and applied to alleviate the local minima problem during the process of finding an optimal solution. The experimental results have shown that the proposed collaborative decision-making model is effective in multimodal emotion recognition. © 2013 IEEE.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings - IEEE International Conference on Multimedia and Expo, 2013, 論文編號 6607472, 2013 IEEE International Conference on Multimedia and Expo, ICME 2013; San Jose, CA; United States; 15 July 2013 到 19 July 2013; 類別編號CFP13ICM-ART; 代碼 100169
dc.subject (關鍵詞) Affective communication; Classification methods; Collaborative decision making; Emotion recognition; Genetic learning; Local minima problems; Multimodal emotion recognition; Optimal solutions; Exhibitions; Decision making
dc.title (題名) Learning collaborative decision-making parameters for multimodal emotion recognition
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/ICME.2013.6607472
dc.doi.uri (DOI) http://dx.doi.org/10.1109/ICME.2013.6607472