一种判别极端学习的相关反馈图像检索方法

黄晓冬 1; 孙亮 1; 刘胜蓝 2

doi:10.7652/xjtuxb201608016

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一种判别极端学习的相关反馈图像检索方法

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- 一种判别极端学习的相关反馈图像检索方法
- A Retrieval Method of Relevance Feedback Images Based on Discriminative Extreme Learning
- 西安交通大学学报 2016年50卷第8期页码：96-102
- 作者机构：
  
  解放军信息工程大学信息系统工程学院,郑州,450001
- 作者简介：
- 基金信息：
  
  : 2016-03-20。作者简介: 黄晓冬(1991—),男,硕士生;孙亮(通信作者),男,教授。基金项目: 国家自然科学基金资助项目(61372172)
- DOI：10.7652/xjtuxb201608016
  中图分类号： TP391
- 网络首发：2016-08-10，
  
  纸质出版：2016
- 稿件说明：
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黄晓冬 1, 孙亮 1, 刘胜蓝 2. 一种判别极端学习的相关反馈图像检索方法[J]. 西安交通大学学报, 2016,50(8):96-102.

A Retrieval Method of Relevance Feedback Images Based on Discriminative Extreme Learning[J]. 2016, 50(8): 96-102.
黄晓冬 1, 孙亮 1, 刘胜蓝 2. 一种判别极端学习的相关反馈图像检索方法[J]. 西安交通大学学报, 2016,50(8):96-102. DOI： 10.7652/xjtuxb201608016.

A Retrieval Method of Relevance Feedback Images Based on Discriminative Extreme Learning[J]. 2016, 50(8): 96-102. DOI： 10.7652/xjtuxb201608016.

摘要

针对基于支持向量机(SVM)的相关反馈图像检索方法计算复杂度高、缺乏判别能力以及图像特征提取不充分的问题

提出一种基于判别极端学习的相关反馈图像检索(DELM)方法。在图像特征提取阶段

通过连接图像的颜色、纹理及边缘直方图实现图像的特征提取

解决了以往多数检索方法仅使用单一图像特征造成的图像描述不充分的问题; 在检索的反馈阶段

将最大边际准则(MMC)引入到极端学习机中

通过分析极端学习机隐层空间的类内离散度和类间离散度得到包含判别信息的分类模型

并给出降维和不降维两种形式

以提高相关反馈图像检索系统的检索能力。DELM方法能有效应用于基于内容的图像检索中

并显著提高图像检索的性能。实验结果表明

DELM方法和采用SVM、ELM和最小类别方差ELM的方法相比

在Corel-1K数据集下检索平均准确率分别提高了11.06%、5.28%和6.40%。

Abstract

A novel retrieval method of relevance feedback images based on discriminative extreme learning

named DELM

is proposed to concern the high computational complexity

low discriminant ability and insufficient image feature extraction of content-based image retrieval(CBIR)with the relevance feedback(RF)methods based on support vector machine(SVM). The proposed method extracts image features in the phase of image feature extraction through color

texture and edge histogram of the image to solve the problem that image feature extraction of the existing methods that based on single feature is insufficient. A maximum margin criterion(MMC)is introduced to extreme learning machine(ELM)in the phase of retrieval feedback. A classification model including discriminative information is obtained through analyzing discrete degrees within and between scatters of feature space in ELM hidden layer

and two versions of DELM are proposed to improve the retrieval performance of RF based image retrieval system

that is

a dimension reduction free based version and a dimension reduction based version. The DELM method is effectively applied to CBIR

and significantly improves the quality of retrieval performance. Experimental results on Corel-1K dataset and comparisons with the methods using SVM

ELM

and minimum class variance ELM(MCVELM)show that the average retrieval precision of the DELM method increases by about 11.06%

5.28% and 6.40%

respectively.

关键词

Keywords

references

HE X. Incremental semi-supervised subspace learning for image retrieval [C]∥Proceedings of the 12th Annual ACM International Conference on Multimedia. New York, USA: ACM, 2004: 2-8.

KUNDU M K, CHOWDHURY M, BUL S R. A graph-based relevance feedback mechanism in content-based image retrieval [J]. Knowledge-Based Systems, 2015, 73: 254-264.

HOI S C H, JIN R, ZHU J, et al. Semi-supervised SVM batch mode active learning for image retrieval [C]∥IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2008: 1-7.

WANG X Y, CHEN J W, YANG H Y. A new integrated SVM classifiers for relevance feedback content-based image retrieval using EM parameter estimation [J]. Applied Soft Computing, 2011, 11(2): 2787-2804.

HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: theory and applications [J]. Neurocomputing, 2006, 70(1): 489-501.

HUANG G B, CHEN L, SIEW C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes [J]. IEEE Transactions on Neural Networks, 2006, 17(4): 879-892.

HUANG G B, CHEN L. Convex incremental extreme learning machine [J]. Neurocomputing, 2007, 70(16): 3056-3062.

HORATA P, CHIEWCHANWATTAN S, SUNAT K. Robust extreme learning machine [J]. Neurocomputing, 2013, 102: 31-44.

LIU S, FENG L, XIAO Y, et al. Robust activation function and its application: semisupervised kernel extreme learning method [J]. Neurocomputing, 2014, 144: 318-328.

冯林, 刘胜蓝, 张晶, 等. 高维数据中鲁棒激活函数的极端学习机及线性降维 [J]. 计算机研究与发展, 2014, 51(6): 1331-1340.

FENG Lin, LIU Shenglan, ZHANG Jing, et al. Robust activation function of extreme learning machine and linear dimensionality reduction in high-dimensional data [J]. Journal of Computer Research and Development, 2014, 51(6): 1331-1340.

LIU S, WANG H, WU J, et al. Incorporate extreme learning machine to content-based image retrieval with relevance feedback [C]∥Proceeding of the 11th World Congress on Intelligent Control and Automation. Piscataway, NJ, USA: IEEE, 2014: 1010-1013.

IOSIFIDIS A, TEFAS A, PITAS I. Minimum class variance extreme learning machine for human action recognition [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(11): 1968-1979.

LI H, JIANG T, ZHANG K. Efficient and robust feature extraction by maximum margin criterion [J]. IEEE Transactions on Neural Networks, 2006, 17(1): 157-165.

HUANG G B, ZHOU H, DING X, et al. Extreme learning machine for regression and multiclass classification [J]. IEEE Transactions on Systems, Man, and Cybernetics: Part B Cybernetics, 2012, 42(2): 513-529.

张磊, 林福宗. 基于支持向量机的相关反馈图像检索算法 [J]. 清华大学学报(自然科学版), 2002, 42(1): 80-83.

ZHANG Lei, LIN Fuzong. Support vector machine based relevance feedback algorithm in image retrieval [J]. Journal of Tsinghua University(Science and Technology), 2002, 42(1): 80-83.

OJALA T, PIETIKÄINEN M, MÄENPÄÄ T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.

LIU G H, LI Z Y, ZHANG L, et al. Image retrieval based on micro-structure descriptor [J]. Pattern Recognition, 2011, 44(9): 2123-2133.

LIU G H, YANG J Y, LI Z Y. Content-based image retrieval using computational visual attention model [J]. Pattern Recognition, 2015, 48(8): 2554-2566.

周远,周玉生,刘权,等.一种适用于图像拼接的DSIFT算法研究.2015,49(9):84-90.[doi:10.7652/xjtuxb201509015]

毛彦斌,张选平,杨晓刚.伪DNA密码图像加密算法研究.2015,49(9):91-98.[doi:10.7652/xjtuxb201509016]

刘凯,张立民,孙永威,等.利用深度玻尔兹曼机与典型相关分析的自动图像标注算法.2015,49(6):33-38.[doi:10.7652/xjtuxb201506006]

符均,牟轩沁,季文博.亮色分离的饱和图像校正方法.2014,48(10):101-107.[doi:10.7652/xjtuxb201410016]

岳桂华,滕奇志,何小海,等.岩心三维图像修复算法.2014,48(9):37-42.[doi:10.7652/xjtuxb201409007]

任茂栋,梁晋,唐正宗,等.数字图像相关法中的优化插值滤波器.2014,48(7):65-70.[doi:10.7652/xjtuxb201407012]

靳峰,冯大政.利用空间序列描述子的快速准确的图像配准算法.2014,48(6):19-24.[doi:10.7652/xjtuxb201406004]

张琳彦,朱利.以多幅图像非几何约束线段匹配重建建筑物外立面三维线段模型.2014,48(4):15-19+25.[doi:10.7652/xjtuxb201404003]

袁飞,朱利,张磊.利用超图图割的图像共分割算法.2014,48(2):20-24.[doi:10.7652/xjtuxb201402004]

王芳梅,范虹,YiWANG.利用改进CV模型连续水平集算法的核磁共振乳腺图像分割.2014,48(2):38-43.[doi:10.7652/xjtuxb201402007]

马元魁,张树生,白晓亮.用球面混合曲率图像比较三角网格模型的相似性.2012,46(5):97-101.[doi:10.7652/xjtuxb 201205017]

史金钢,齐春.一种双约束稀疏模型图像修复算法.2012,46(2):6-10.[doi:10.7652/xjtuxb201202002]

吴攀超,王宗义,林欣堂.采用局部差分模型描述的彩色图像配准技术.2011,45(10):30-37.[doi:10.7652/xjtuxb201110 006]

徐胜军,韩九强,赵亮,等.用于图像分割的局部区域能量最小化算法.2011,45(8):7-12.[doi:10.7652/xjtuxb201108 002]

罗涛,牟轩沁.一种胸部X射线摄影图像中结节检测的多尺度匹配滤波器.2011,45(4):30-35.[doi:10.7652/xjtuxb 201104006]

赵海峰,姚丽莎,罗斌.改进的人工鱼群算法和Powell法结合的医学图像配准.2011,45(4):46-52.[doi:10.7652/xjtuxb201104009]

陈蓉,孙剑,徐宗本.彩色图像分割中基于图上半监督学习算法研究.2011,45(2):11-14.[doi:10.7652/xjtuxb201102003]

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