基于PDEs的图像特征提取方法

doi:10.6040/j.issn.1672-3961.0.2018.072

山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (4): 27-36.doi: 10.6040/j.issn.1672-3961.0.2018.072

基于PDEs的图像特征提取方法

江珊珊,杨静^*,范丽亚

聊城大学数学科学学院, 山东聊城 252059

收稿日期:2018-02-09 出版日期:2018-08-20 发布日期:2018-02-09
通讯作者: 杨静(1986— ),女,山东聊城人,讲师,博士,主要研究方向为模式识别理论与应用,机器学习理论与应用. ;E-mail: yangjing860204@163.com E-mail:jiangshanshan102@163.com
作者简介:江珊珊(1995— ),女,山东青岛人,硕士研究生,主要研究方向为机器学习理论与应用,模式识别. E-mail:jiangshanshan102@163.com
基金资助:
山东省自然科学基金资助项目(ZR2016AM24,ZR2018BF010);聊城大学博士基金资助项目(318051715)

An image feature extraction method based on PDEs

JIANG Shanshan, YANG Jing^*, FAN Liya

School of Mathematical Sciences, Liaocheng University, Liaocheng 252059, Shandong, China

Received:2018-02-09 Online:2018-08-20 Published:2018-02-09

摘要/Abstract

摘要： 针对提取图像判别信息的基于偏微分方程(partial differential equations, PDEs)的方法做了进一步研究。研究进化次数对图像特征质量的影响和压缩函数的压缩速度对图像特征质量的影响。试验结果表明:PDEs的进化可以降低遮挡的影响以及对光暗具有鲁棒性,但PDEs的进化次数以及压缩函数和压缩速度严重影响图像特征质量。

关键词: 偏微分方程, 压缩函数, 图像特征, 遮挡, 进化次数

Abstract: Further research was conducted on image feature extraction method based on partial differential equations(PDEs). The effect of evolution times on quality of feature, and the reflection of compression function on the quality of feature were studied. Experiment results indicated that the evolution of PDEs could reduce the impact of occlusion and be robust to dark light, but the qualities of the image features could be seriously affected by evolution times of PDEs and compression function and compression speed.

Key words: partial differential equation, occlusion, compression functions, evolution time, image feature

中图分类号:

TP391

江珊珊,杨静,范丽亚. 基于PDEs的图像特征提取方法[J]. 山东大学学报(工学版), 2018, 48(4): 27-36.

JIANG Shanshan, YANG Jing, FAN Liya. An image feature extraction method based on PDEs[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(4): 27-36.

参考文献 18

[1]	GAUDIOSO M, GORGONE E, LABBE M, et al. Lagrangian relaxation for SVM feature selection[J]. Computers and Operations Research, 2017, 87:137-145.
[2]	YUAN Youli, ZHANG Min, LUO Pengfei, et al. SVM-based detection in visible light communications[J]. Optik, 2017, 151:55-64.
[3]	AFFONSO C, ROSSI A L, VIEIRA F H, et al. Deep learning for biological image classification[J]. Expert Systems with Applications, 2017, 85:114-122.
[4]	YANG Meng, CHANG Heyou, LUO Weixin, et al. Fisher discrimination dictionary pair learning for image classification[J]. Neurocomputing, 2017, 269:13-20.
[5]	LI Haojie, LIU Lijuan, SUN Fuming, et al. Multi-level feature representations for video semantic concept detection[J]. Neurocomputing, 2016, 172:64-70.
[6]	LU Bo, WANG Guoren, YUAN Ye, et al. Semantic concept detection for video based on extreme learning machine[J]. Neurocomputing, 2013, 102:176-183.
[7]	HUANG Sheng, YANG Dan, GE Yongxin, et al. Combined supervised information with PCA via discriminative component selection[J]. Information Processing Letters, 2015, 115:812-816.
[8]	崔燕,范丽亚. 高维数据正定核与不定核的KPCA变换阵比较[J]. 山东大学学报(工学版), 2011, 41(1):17-24. CUI Yan, FAN Liya. Comparison of KPCA transformation matrices with definite and indefinite kernels for high-dimensional data[J]. Journal of Shandong university(Engineering science), 2011, 41(1):17-24.
[9]	GYAMFI K S, BRUSEY J, HUNT A, et al. Linear classifier design under heteroscedasticity in linear discriminant analysis[J]. Expert Systems with Applications, 2017, 79:44-52.
[10]	杨静, 范丽亚. 基于模糊隶属度的加权广义不定核判别分析[J]. 山东大学学报(工学版), 2012, 42(3):31-38. YANG Jing, FAN Liya. Weighted generalize indefinite kernel discriminant analysis based on fuzzy membership[J]. Journal of Shandong university(Engineering science), 2012, 42(3):31-38.
[11]	LENC L, KRAL P. Automatic face recognition system based on the SIFT features[J]. Computers and Electrical Engineering, 2015, 46:256-272.
[12]	ALILOU V K, YAGHMAEE F. Non-texture image inpainting using histogram of oriented gradients[J]. Journal of Visual Communication & Image Representation, 2017, 48:43-53.
[13]	BRUNA J, MALLAT S. Invariant scattering convolution networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8):1872-1886.
[14]	LIU Risheng, LIN Zhouchen, ZHANG Wei, et al. Toward designing intelligent PDEs for computer vision: an optimal control approach[J]. Image and Vision Computing, 2013, 31(1):43-56.
[15]	LIU Risheng, ZHONG Guangyu, ZHOU Pan, et al. Learning to Diffuse: A new perspective to design PDEs for visual analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(12):2457-2471.
[16]	FANG Cong, ZHAO Zhenyu, ZHOU Pan, et al. Feature learning via partial differential equation with applications to face recognition[J]. Pattern Recognition, 2017, 69:14-25.
[17]	GEORGHIADES A, BELHUMEUR P, KRIEGMAN D. From few to many: illuminationcone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6):643-660.
[18]	SAUL L K, WEINBERGER K Q, HAM J H, et al. Spectral methods for dimensionality reduction[J]. Semisupervised Learning, 2006:293-308.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于PDEs的图像特征提取方法

An image feature extraction method based on PDEs

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献 18

相关文章 8

多维度评价

本文评价

推荐阅读 10

[1]	陈浩然,祁鑫,田志浩,李桐,刘刚,李常刚. 考虑日食过境影响的光伏出力与爬坡速率预测方法[J]. 山东大学学报 (工学版), 2025, 55(5): 70-77.
[2]	范黎林,刘士豪,李源,毛文涛,陈宗涛. 基于课程正则化的物理信息神经网络渐进式训练策略[J]. 山东大学学报 (工学版), 2024, 54(1): 11-24.
[3]	余明骏,刁红军,凌兴宏. 基于轨迹掩膜的在线多目标跟踪方法[J]. 山东大学学报 (工学版), 2023, 53(2): 61-69.
[4]	刘新锋, 张旖旎,徐惠三,宋玲,陈梦雅. 基于随机森林和专家系统的分布式光伏电站阴影遮挡诊断[J]. 山东大学学报 (工学版), 2021, 51(2): 98-104.
[5]	戚世乐,王美清. 自适应分割弱边缘的活动轮廓模型[J]. 山东大学学报(工学版), 2013, 43(6): 17-20.
[6]	王江宁, 纪力强. 昆虫图像特征研究[J]. 山东大学学报(工学版), 2011, 41(2): 51-57.
[7]	方挺,杨忠,沈春林 . 无人机编队视频序列中的多目标精确跟踪[J]. 山东大学学报(工学版), 2008, 38(4): 22-26 .
[8]	马丽,常发亮,乔谊正 . 基于遗传算法和粒子滤波器的非刚性目标跟踪[J]. 山东大学学报(工学版), 2006, 36(3): 26-29 .