TY - CHAP
T1 - Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding
AU - Chen, Luefeng
AU - Wu, Min
AU - Pedrycz, Witold
AU - Hirota, Kaoru
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85096209726&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61577-2_8
DO - 10.1007/978-3-030-61577-2_8
M3 - Chapter
AN - SCOPUS:85096209726
T3 - Studies in Computational Intelligence
SP - 115
EP - 131
BT - Studies in Computational Intelligence
PB - Springer Science and Business Media Deutschland GmbH
ER -