|
|||||
|
|
| 基于核函数的高阶特征表示方法 | |
| 引用本文： | 解晓萌,梁 鹏,王 成,吴伟文.基于核函数的高阶特征表示方法[J].计算机应用研究,2012,29(11):4350-4352. |
| 作者姓名： | 解晓萌 梁 鹏 王 成 吴伟文 |
| 作者单位： | 1. 华南理工大学 计算机科学与工程学院,广州,510006 2. 广东技术师范学院 计算机科学学院,广州,510665 3. 仲恺农业工程学院 计算机科学与工程学院,广州,510225 |
| 基金项目： | 国家自然科学基金资助项目（60273064） |
| 摘 要： | 为了解决高阶局部特征带来的计算复杂度提高问题,提出一种基于核函数的高阶局部特征表示方法。通过在两幅图像的局部特征之间进行比较,将特征空间映射到几何不变空间,统计高阶局部特征构建核函数,并结合支持向量机进行多类目标图像分类实验。实验结果分析表明,该方法在提高分类准确率的同时,所需的计算时间只与局部特征的个数呈线性增长。 |
| 关 键 词： | 高阶局部特征 图像分类 核函数 |
High order local feature representation method based onkernel function |
|
| XIE Xiao-meng , LIANG Peng , WANG Cheng , WU Wei-wen.High order local feature representation method based onkernel function[J].Application Research of Computers,2012,29(11):4350-4352. | |
| Authors: | XIE Xiao-meng LIANG Peng WANG Cheng WU Wei-wen |
| Affiliation: | 1. School of Computer Science & Engineering, South China University of Technology, Guangzhou 510006, China; 2. School of Computer Science, GuangDong Polytechnic Normal University, Guangzhou 510665, China; 3 School of Computer Science & Engineering, Zhongkai University of Agriculture & Engineering, Guangzhou 510225, China |
| Abstract: | In order to solve the computation increase caused by high order local features, this paper proposed a novel high order local feature representation approach based on kernel function. It first compared local features extracted from two difference images. Then transformed the features in the two images to the geometry invariant spaces. Next, it constructed kernel function by counting high order features. Finally, it performed the multi object classification experiment by using support vector machine. The experimental results show that proposed method has better precision and its computation time is linear to the number of local features in images. |
| Keywords: | high order local feature image classification kernel function |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |
| 点击此处可从《计算机应用研究》浏览原始摘要信息 | |
| 点击此处可从《计算机应用研究》下载全文 | |