光电工程, 2009, 36 (3): 33, 网络出版: 2009-10-09   

基于局部特征空间相关核的图像目标分类

Local Feature Spatial Correlation Kernel for Image Object Classification
作者单位
中国科学技术大学 电子工程与信息科学系,合肥 230027
摘要
为了描述局部特征在图像空间中相对位置关系,提出一种局部特征空间相关核(Spatial Correlation Kernel, SCK)用于图像目标分类。该方法首先提取并量化图像中的局部特征,再计算量化后的局部特征的空间位置自相关度,然后利用直方图交叉匹配两幅图像的空间位置自相关度得到局部特征空间相关核。该核充分利用局部特征的强分辨能力及其空间位置,且SCK 具有线性计算复杂度,满足正定条件,可以运用于基于核的学习算法。本文将SCK 嵌入支持向量机对公共数据库中图像目标进行分类,实验结果表明,SCK 可以获得良好的时间效率和分类性能。
Abstract
For representing the relative location relationship of local features in the image space, local feature Spatial Correlation Kernel (SCK) is proposed for image object classification. The local features in the image are extracted and quantized, and the spatial location auto-correlations are calculated for vector-quantized local features, and then the histogram intersection is used to match spatial location auto-correlations of two images to obtain the local feature spatial correlation kernel. The proposed kernel makes good use of both the powerfully discriminative ability of local features and their spatial locations. Furthermore, SCK has a linear computation cost, satisfies the positive definite condition and could be used for kernel-based learning algorithms. The experiments performed on the public image database by embedding SCK into the support vector machine to classify the image objects demonstrate that SCK achieves the good time efficiency and the good classification performance.

陈海林, 吴秀清, 胡俊华. 基于局部特征空间相关核的图像目标分类[J]. 光电工程, 2009, 36(3): 33. CHEN Hai-lin, WU Xiu-qing, HU Jun-hua. Local Feature Spatial Correlation Kernel for Image Object Classification[J]. Opto-Electronic Engineering, 2009, 36(3): 33.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!