Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding

Luefeng Chen; Min Wu; Witold Pedrycz; Kaoru Hirota

doi:10.1007/978-3-030-61577-2_8

Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding

Luefeng Chen^*, Min Wu, Witold Pedrycz, Kaoru Hirota

^*此作品的通讯作者

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

1 引用（Scopus）

摘要

The Dempster-Shafer (D-S) theory based on multi-SVM to deal with multimodal gesture images for intention understanding is proposed, in which the Sparse Coding (SC) based Speeded-Up Robust Features (SURF) are used for feature extraction of depth and RGB image. Aiming at the problems of the small sample, high dimensionality and feature redundancy for image data, we use the SURF algorithm to extract the features of the original image, and then perform their Sparse Coding, which means that the image is subjected to two-dimensional feature reduction. The dimensionally reduced gesture features are used by the multi-SVM for classification.

源语言	英语
主期刊名	Studies in Computational Intelligence
出版商	Springer Science and Business Media Deutschland GmbH
页	115-131
页数	17
DOI	https://doi.org/10.1007/978-3-030-61577-2_8
出版状态	已出版 - 2021
已对外发布	是

出版系列

姓名	Studies in Computational Intelligence
卷	926
ISSN（印刷版）	1860-949X
ISSN（电子版）	1860-9503

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10.1007/978-3-030-61577-2_8

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Chen, L., Wu, M., Pedrycz, W., & Hirota, K. (2021). Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding. 在 Studies in Computational Intelligence (页码 115-131). (Studies in Computational Intelligence; 卷 926). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61577-2_8

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abstract = "The Dempster-Shafer (D-S) theory based on multi-SVM to deal with multimodal gesture images for intention understanding is proposed, in which the Sparse Coding (SC) based Speeded-Up Robust Features (SURF) are used for feature extraction of depth and RGB image. Aiming at the problems of the small sample, high dimensionality and feature redundancy for image data, we use the SURF algorithm to extract the features of the original image, and then perform their Sparse Coding, which means that the image is subjected to two-dimensional feature reduction. The dimensionally reduced gesture features are used by the multi-SVM for classification.",

author = "Luefeng Chen and Min Wu and Witold Pedrycz and Kaoru Hirota",

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year = "2021",

doi = "10.1007/978-3-030-61577-2_8",

language = "English",

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Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding. / Chen, Luefeng; Wu, Min; Pedrycz, Witold 等.
Studies in Computational Intelligence. Springer Science and Business Media Deutschland GmbH, 2021. 页码 115-131 (Studies in Computational Intelligence; 卷 926).

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

TY - CHAP

T1 - Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding

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AU - Wu, Min

AU - Pedrycz, Witold

AU - Hirota, Kaoru

PY - 2021

Y1 - 2021

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AB - The Dempster-Shafer (D-S) theory based on multi-SVM to deal with multimodal gesture images for intention understanding is proposed, in which the Sparse Coding (SC) based Speeded-Up Robust Features (SURF) are used for feature extraction of depth and RGB image. Aiming at the problems of the small sample, high dimensionality and feature redundancy for image data, we use the SURF algorithm to extract the features of the original image, and then perform their Sparse Coding, which means that the image is subjected to two-dimensional feature reduction. The dimensionally reduced gesture features are used by the multi-SVM for classification.

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Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding

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