摘要
提出一种基于广义性多核学习的静态图像人体行为识别方法。从图像中提取基于边缘的梯度方向直方图和基于稠密采样的尺度不变特征描述子,并使用空间金字塔模型加入粗略空间信息;运用直方图内交核函数计算金字塔模型各层核矩阵,通过广义性多核学习方法求解各个核矩阵权重,以线性组合方式得到最优核矩阵;最后利用多核学习决策函数进行行为识别。Willow-actions数据集实验结果表明,本文方法比其他几种方法更加有效。
A novel action recognition method based on general multiple kernel learning is proposed.Firstly,histogram of oriented gradients（HOG）based on edge of image and scale invariant feature transform（SIFT）based on dense sampling are extracted.Furthermore,spatial pyramid model is considered to obtain coarse spatial information.Then,the kernel matrix of each level in spatial model is computed by histogram intersection kernel function.With general multiple kernel learning,the weights of kernel matrixes are solved and the optimal kernel matrix is achieved by the linear combination of kernel matrixes.Finally,action recognition is realized by the decision function.The obtained impressive result shows that the proposed algorithm is more effective than some common methods in Willow-actions dataset.
出处
《数据采集与处理》
CSCD
北大核心
2016年第5期958-964,共7页
Journal of Data Acquisition and Processing
基金
高等学校博士学科点专项科研基金(20121401120015)资助项目
国家自然科学基金(61201453)资助项目
山西省自然科学基金(2012011014-4)资助项目
关键词
行为识别
广义性多核学习
空间金字塔模型
直方图内交核函数
action recognition
general multiple kernel learning
spatial pyramid model
histogram intersection kernel function
