Gesture recognition based on an improved local sparse representation classification algorithm
Gesture recognition based on an improved local sparse representation classification algorithm
复制标题
基于改进的局部稀疏表示分类算法的手势识别
DOI：
10.1007/s10586-017-1237-1
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发表时间：
2019-09-01
影响因子：
4.4
通讯作者：
Liu, Honghai
中科院分区：
文献类型：
--
作者：
He, Yang;Li, Gongfa;Liu, Honghai
稀疏表示分类方法以其良好的识别效果和分类性能在模式识别中得到了广泛的关注和研究。采用最小化范数求解稀疏系数，选择所有训练样本作为冗余字典进行计算，但计算复杂度较高。针对基于范数求解算法计算复杂度高的问题，提出了范数局部稀疏表示分类算法。该算法采用最小范数方法选择局部字典。然后利用字典中的最小范数求解稀疏系数进行分类，并在构建的手势数据库上验证该算法对手势的识别效果。实验结果表明，该算法能在保证识别率的前提下有效减少计算时间，性能略优于KNN-SRC算法。
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 minimizednorm 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 thenorm based solving algorithm,norm local sparse representation classification algorithm is proposed. This algorithm uses the minimumnorm method to select the local dictionary. Then the minimumnorm 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.