Paper Info
Title
Gesture recognition based on an improved local sparse representation classification algorithm
Abstract
The sparse representation classification method has been widely concerned and studied in pattern recognition because of its good recognition effect and classification performance. Using the minimized \(l_{1}\) norm to solve the sparse coefficient, all the training samples are selected as the redundant dictionary to calculate, but the computational complexity is higher. Aiming at the problem of high computational complexity of the \(l_{1}\) norm based solving algorithm, \(l_{2}\) norm local sparse representation classification algorithm is proposed. This algorithm uses the minimum \(l_{2}\) norm method to select the local dictionary. Then the minimum \(l_{1}\) norm is used in the dictionary to solve sparse coefficients for classify them, and the algorithm is used to verify the gesture recognition on the constructed gesture database. The experimental results show that the algorithm can effectively reduce the calculation time while ensuring the recognition rate, and the performance of the algorithm is slightly better than KNN-SRC algorithm.
Year
DOI
Venue
2019
10.1007/s10586-017-1237-1
Cluster Computing
Keywords
DocType
Volume
Gesture recognition, $$l_{2}$$l2 norm, Sparse representation, Classification algorithm
Journal
22
Issue
ISSN
Citations 
5
1573-7543
13
PageRank 
References 
Authors
0.53
13
10
Name
Order
Citations
PageRank
Yang He1130.87
Gongfa Li223943.45
Yajie Liao3130.53
Ying Sun429140.03
Jianyi Kong5391.89
Guozhang Jiang617227.25
Du Jiang79714.40
Bo Tao8522.43
Shuang Xu927432.53
Honghai Liu101974178.69