基于双重鉴别相关性分析的图像识别算法

doi:10.11772/j.issn.1001-9081.2016.03.713

计算机应用 ›› 2016, Vol. 36 ›› Issue (3): 713-717.DOI: 10.11772/j.issn.1001-9081.2016.03.713

基于双重鉴别相关性分析的图像识别算法

李晋, 钱旭

中国矿业大学(北京) 机电与信息工程学院, 北京 100083

收稿日期:2015-10-27 修回日期:2015-12-02 出版日期:2016-03-10 发布日期:2016-03-17
通讯作者: 李晋
作者简介:李晋(1985-),男,山西大同人,博士研究生,主要研究方向:人工智能、模式识别及机器学习;钱旭(1962-),男,北京人,教授,博士,主要研究方向:数据库、信息融合、软件工程、计算机支持的协同工作。
基金资助:
国家自然科学基金资助项目(61103171)。

Image recognition algorithm based on dual-view discriminant correlation analysis

LI Jin, QIAN Xu

School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, Beijing 100083, China

Received:2015-10-27 Revised:2015-12-02 Online:2016-03-10 Published:2016-03-17
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61103171).

摘要/Abstract

摘要： 针对多视图相关性算法未有效利用视图中相关信息且忽视了潜在的鉴别信息的问题,提出基于同一视图内和不同视图间的双重鉴别相关性分析(DVDCA)算法。首先,设计有监督的类内和类间相关性变量,通过最大化类内相关性变量、最小化类间相关性变量来提取视图中的鉴别特征;其次,考虑在同一视图内和不同视图间均考虑进行鉴别相关特征提取,设计约束形式的双重视图鉴别相关性特征提取模型,以利用丰富的视图信息。在Multi-PIE多角度人脸数据集数据集上与多视图线性鉴别分析、典型相关性分析(CCA)、多视图鉴别隐性空间(MDLS)、不相关多视图鉴别字典学习(UMDDL)四种算法对比实验,DVDCA分类识别率能够提高1.45~4.73个百分点;在MFD多特征手写体数据集上分类识别率能够提高1.25~5.29个百分点。

关键词: 鉴别分析, 图像识别, 多视图学习, 典型相关性分析, 信息挖掘, 双重鉴别分析

Abstract: Focusing on the issue that multi-view correlation analysis are not effective to exploit the correlation information and neglect latent discriminant information in images, a Dual-View Discriminant Correlation Analysis (DVDCA) approach based on dual view was proposed. Firstly, the supervised within-class correlation variation and between-class correlation variation were designed; secondly, within-class correlation variation was maximized and between-class correlation variation was minimized to extract the discriminant feature; finally, constrained dual-view discriminant correlation model was designed to exploit rich view information of both within-view and between-view. Compared with multi-view linear discriminant analysis, Canonical Correlation Analysis (CCA), Multi-view Discriminant Latent Space (MDLS), Uncorrelated Multi-view Discrimination Dictionary Learning (UMDDL) on the Multi-PIE dataset, the proposed algorithm can achieve recognition rate increase of 1.45-4.73 percentage points; on the MFD dataset, the proposed algorithm can achieve increase of 1.25-5.29 percentage points.

Key words: discriminant analysis, image recognition, multi-view learning, Canonical Correlation Analysis (CCA), information mining, dual-view discriminant analysis

中图分类号:

TP391.4

李晋, 钱旭. 基于双重鉴别相关性分析的图像识别算法[J]. 计算机应用, 2016, 36(3): 713-717.

LI Jin, QIAN Xu. Image recognition algorithm based on dual-view discriminant correlation analysis[J]. Journal of Computer Applications, 2016, 36(3): 713-717.

参考文献

[1] CABRAL R, DE LA TORRE F, COSTEIRA J P, et al. Matrix completion for weakly-supervised multi-label Image classification [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(1):121-135.
[2] HUANG Y, WU Z, WANG L, et al. Feature coding in image classification: a comprehensive study [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014,36(3):493-506.
[3] SHAO L, LIU L, LI X. Feature learning for image classification via multiobjective genetic programming [J]. IEEE Transactions on Neural Networks and Learning Systems, 2014,25(7):1359-1371.
[4] CHEN L. A fair comparison should be based on the same protocol—comments on "trainable convolution filters and their application to face recognition" [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014,36(3):622-623.
[5] CAMPS-VALLS G, TUIA D, BRUZZONE L, et al. Advances in hyperspectral image classification: earth monitoring with statistical learning methods [J]. IEEE Signal Processing Magazine, 2014,31(1):45-54.
[6] TORRIONE P A, MORTON K D, SAKAGUCHI R, et al. Histograms of oriented gradients for landmine detection in ground-penetrating radar data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(3):1539-1550.
[7] LI W, CHEN C, SU H, et al. Local binary patterns and extreme learning machine for hyperspectral imagery classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(7):1-13.
[8] CANDÉS E J, LI X, MA Y, et al. Robust principal component analysis? [J]. Journal of the ACM, 2011,58(3): Article No. 11.
[9] SHU X, GAO Y, LU H. Efficient linear discriminant analysis with locality preserving for face recognition [J]. Pattern Recognition, 2012,45(5):1892-1898.
[10] HUH S, FIENBERG S E. Discriminative topic modeling based on manifold learning [J]. ACM Transactions on Knowledge Discovery from Data, 2012,5(4):653-662.
[11] SUN L, JI S, YE J. Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,33(1):194-200.
[12] RUPNIK J, SHAWE-TAYLOR J. Multi-view canonical correlation analysis [EB/OL]. [2015-04-11]. http://ailab.ijs.si/dunja/SiKDD2010/Papers/Rupnik_Final.pdf.
[13] YUAN Y H, SUN Q S. Discriminative learning of multiset integrated canonical correlation analysis for feature fusion [C]//Proceedings of the 2012 15th International Conference on Information Fusion. Piscataway, NJ: IEEE, 2012:882-887.
[14] SHARMA A, KUMAR A, DAUME H, et al. Generalized multiview analysis: a discriminative latent space [C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2012:2160-2167.
[15] KAN M, SHAN S, ZHANG H, et al. Multi-view discriminant analysis [M]//FITZGIBBON A, LAZEBNIK S, PERONA P, et al. Computer Vision—ECCV 2012, LNCS 7572. Berlin: Springer, 2012:808-821.
[16] JING X Y, HU R M, WU F, et al. Uncorrelated multi-view discrimination dictionary learning for recognition [C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2014:2787-2795.

基于双重鉴别相关性分析的图像识别算法

Image recognition algorithm based on dual-view discriminant correlation analysis

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	欧莉莉, 邵峰晶, 孙仁诚, 隋毅. 基于半监督方法的脑梗死图像识别[J]. 计算机应用, 2021, 41(4): 1221-1226.
[2]	张凯悦, 张鸿. 基于注意力机制网络的航运监控图像识别模型[J]. 计算机应用, 2021, 41(10): 3010-3016.
[3]	刘颖, 刘玉霞, 毕萍. 基于边缘学习的低分辨率图像识别算法[J]. 计算机应用, 2020, 40(7): 2046-2052.
[4]	徐代, 岳璋, 杨文霞, 任潇. 基于改进的三向流Faster R-CNN的篡改图像识别[J]. 计算机应用, 2020, 40(5): 1315-1321.
[5]	傅泰铭, 陈燕, 李陶深. 基于线性分配的难负样本挖掘度量学习[J]. 计算机应用, 2020, 40(2): 352-357.
[6]	甘岚, 郭子涵, 王瑶. 基于径向变换和改进AlexNet的胃肿瘤细胞图像识别方法[J]. 计算机应用, 2019, 39(10): 2923-2929.
[7]	邱津怡, 罗俊, 李秀, 贾伟, 倪福川, 冯慧. 基于卷积神经网络的多尺度葡萄图像识别方法[J]. 计算机应用, 2019, 39(10): 2930-2936.
[8]	杨健, 周涛, 郭丽芳, 张飞飞, 梁蒙蒙. 基于布谷鸟搜索和深度信念网络的肺部肿瘤图像识别算法[J]. 计算机应用, 2018, 38(11): 3225-3230.
[9]	曹香滢, 孙卫民, 朱悠翔, 钱鑫, 李晓宇, 业宁. 基于科优先策略的植物图像识别[J]. 计算机应用, 2018, 38(11): 3241-3245.
[10]	冯辉, 荆晓远, 朱小柯. 基于多视图特征投影与合成解析字典学习的图像分类[J]. 计算机应用, 2017, 37(7): 1960-1966.
[11]	陈娟娟, 刘财兴, 高月芳, 梁云. 基于改进特征袋模型的奶牛识别算法[J]. 计算机应用, 2016, 36(8): 2346-2351.
[12]	甘岚, 张永焕. 基于字典学习的正则化鲁棒稀疏表示肿瘤细胞图像识别[J]. 计算机应用, 2016, 36(10): 2895-2899.
[13]	康晓东, 王昊, 郭军, 于文勇. 无监督深度学习彩色图像识别方法[J]. 计算机应用, 2015, 35(9): 2636-2639.
[14]	张玉璞, 杨旗, 张旗. 基于计算机视觉的图像多尺度识别方法[J]. 计算机应用, 2015, 35(2): 502-505.
[15]	邵楠张科. 基于投影熵特征的图像识别算法[J]. 计算机应用, 2013, 33(10): 2874-2877.