[1]YU Siquan,CAO Jiangtao,LI Ping,et al.Hand gesture recognition based on the spatial pyramid bag of features[J].CAAI Transactions on Intelligent Systems,2015,10(3):429-435.[doi:10.3969/j.issn.1673-4785.201405054]
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Hand gesture recognition based on the spatial pyramid bag of features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 429-435 Column: 学术论文—机器感知与模式识别 Public date: 2015-06-25
Title:
Hand gesture recognition based on the spatial pyramid bag of features
Author(s):
YU Siquan1;  CAO Jiangtao1;  LI Ping1;  JI Xiaofei2
1. School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China;
2. School of Automation, Shenyang Aerospace University, Shenyang 110136, China
Keywords:
hand gesture recognition; hand gesture image; scale invariant feature transform (SIFT); spatial pyramid; bag of features; histogram intersection kernel; support vector machines (SVM)
CLC:
TP319
DOI:
10.3969/j.issn.1673-4785.201405054
Abstract:
A novel algorithm based on the spatial pyramid bag of features is proposed to describe the hand image. It is proposed in order to solve the problem that the distribution of feature points cannot be ascertained when using the hand gesture descriptor based on bag of feature of scale invariant feature transform (BoF-SIFT). The capability of the BoF-SIFT can be improved by generating image spatial pyramid. The descriptor can effectively represent the posture by combining the global features and local features of the gesture image, as well as the distribution character of image feature points. Finally, the hand posture recognition is achieved by using the histogram intersection kernel support vector machine (SVM). The experiment on standard database demonstrates the average recognition rate can reach 92.92% for 10 kinds of gestures recognition, verifying the efficiency and effectiveness of the proposed algorithm.
References:
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