AN EFFECTIVE IMAGE RETRIEVAL METHOD BASED ON KERNEL DENSITY ESTIMATION OF COLLAGE ERROR AND MOMENT INVARIANTS 被引量：1

AN EFFECTIVE IMAGE RETRIEVAL METHOD BASED ON KERNEL DENSITY ESTIMATION OF COLLAGE ERROR AND MOMENT INVARIANTS

下载PDF

导出

摘要 In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation (KDE). The proposed method is called CHK (KDE of Collage error and Hu moment) and it is tested on the Vistex texture database with 640 natural images. Experimental results show that the Average Retrieval Rate (ARR) can reach into 78.18%, which demonstrates that the proposed method performs better than the one with parameters respectively as well as the commonly used histogram method both on retrieval rate and retrieval time. In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation （KDE）. The proposed method is called CHK （KDE of Collage error and Hu moment） and it is tested on the Vistex texture database with 640 natural images. Experimental results show that the Average Retrieval Rate （ARR） can reach into 78.18%, which demonstrates that the proposed method performs better than the one with parameters respectively as well as the commonly used histogram method both on retrieval rate and retrieval time.

作者 Zhang Qin Huang Xiaoqing Liu Wenbo Zhu Yongjun Le Jun

机构地区 College of Automation Engineering

出处《Journal of Electronics(China)》 2013年第4期391-400,共10页 电子科学学刊（英文版）

基金 Supported by the Fundamental Research Funds for the Central Universities (No. NS2012093)

关键词 Fractal Coding （FC） Hu moment invariant Kernel Density Estimation （KDE） Variableoptimized bandwidth Image retrieval 图像检索方法核密度估计拼贴误差不变性矩统计方法可变带宽自然图像 KDE

分类号 TP391.41 [自动化与计算机技术—计算机应用技术] O211.67 [理学—概率论与数理统计]

引文网络
相关文献

参考文献1

1邓飙,于传强,李天石,苏文斌,盘仰珂.基于估计点的双窗宽核密度估计算法[J].仪器仪表学报,2011,32(3):615-620. 被引量：14

二级参考文献15

1JONES M C, MARRON J S, SHEATHER S J. A brief survey of bandwidth selection for density estimation [ J ]. Journal of the American Statistical Association, 1996,91 (433) :401-407.
2SAIN S R, BAGGERLY K A, SCOTF D W. Cross-validation of multivariate densities [ J ]. Journal of the American Statistical Association, 1994,89 (427) :807-817.
3BOLANCE C, GUILLEN M, NIELSEN J P. Kernel density estimation of actuarial loss functions [ J ]. Mathematics and Economics, 2003,32.19-36.
4FUKUNAGA K, HOSTETLER L D. The estimation of the gradient of a density function with applications in pattern recognition[ J]. IEEE Trans. Information Theory, 1975, 21:32-40.
5HALL P, KANG K. Bandwidth choice for nonparametric classification [ J ]. The Annals of Statistics, 2005, 33 ( 1 ) :284-306.
6SCOTT D W, SAIN S R. Multidimensional density estimation[ J]. Handbook of Statistics, 2005:229-261.
7KARUNAMUNI R J, ALBERTS T. On boundary correction in kernel density estimation [ J ]. Statistical Methodology, 2005,2(3) :191-212.
8BROWME M. A geometric approach to non-parametric density estimation [ J ]. Pattern Recognition, 2007,40 ( 1 ) : 134-140.
9CAO R, JANSSEN P, VERAVERBEKE N. Relative density estimation and local bandwidth selection for censored data[ J ]. Computational Statistics & Data Analysis, 2001,36(4) :497-510.
10JEFFREYS H. Theory of Probability (3nd. ed) [ M]. London : Oxford Univ. Press, 1997.