具有普适性的改进非负矩阵分解图像特征提取方法

doi:10.11772/j.issn.1001-9081.2017061394

计算机应用 ›› 2018, Vol. 38 ›› Issue (1): 233-237.DOI: 10.11772/j.issn.1001-9081.2017061394

• 虚拟现实与多媒体计算 • 上一篇下一篇

具有普适性的改进非负矩阵分解图像特征提取方法

贾旭¹, 孙福明¹, 李豪杰², 曹玉东¹

1. 辽宁工业大学电子与信息工程学院, 辽宁锦州 121001;
2. 大连理工大学软件学院, 辽宁大连 116024

收稿日期:2017-06-07 修回日期:2017-08-05 发布日期:2018-01-22 出版日期:2018-01-10
通讯作者: 贾旭
作者简介:贾旭(1983-),男,辽宁开原人,副教授,博士,CCF会员,主要研究方向:模式识别、机器学习;孙福明(1972-),男,辽宁大连人,教授,博士,主要研究方向:多媒体处理、机器学习;李豪杰(1973-),男,辽宁大连人,教授,博士,主要研究方向:多媒体处理、计算机视觉;曹玉东(1971-),男,辽宁昌图人,副教授,博士,主要研究方向:模式识别、图像处理。
基金资助:
国家自然科学基金资助项目（61502216，61572244）。

Image feature extraction method based on improved nonnegative matrix factorization with universality

JIA Xu¹, SUN Fuming¹, LI Haojie², CAO Yudong¹

1. School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121001, China;
2. School of Software Technology, Dalian University of Technology, Dalian Liaoning 116024, China

Received:2017-06-07 Revised:2017-08-05 Online:2018-01-22 Published:2018-01-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61502216, 61572244).

摘要/Abstract

摘要： 为提高图像特征提取的普适性，提出了一种基于改进非负矩阵分解（NMF）的图像特征提取方法。首先，考虑到提取的图像特征的实际意义，选用非负矩阵分解模型进行图像特征的降维处理；其次，为实现用较小数量系数来描述图像特征，将稀疏约束作为非负矩阵分解模型的正则项之一；然后，为使降维后优化得到的特征具有较好的类间区分性，将聚类属性作为非负矩阵分解的另一个正则项；最后，通过对模型的梯度下降优化求解，获得最优的特征基向量与图像特征向量。实验结果表明，针对3种图像数据库，所提的图像特征更有利于图像正确分类或识别，错误接受率（FAR）与错误拒绝率（FRR）分别可以降低到0.021与0.025。

关键词: 非负矩阵分解, 特征提取, 稀疏表示, 梯度下降法, 特征降维

Abstract: To improve the universality of image feature extraction, an image feature extraction method based on improved Nonnegative Matrix Factorization (NMF) was proposed. Firstly, considering the practical significance of extracted image features, NMF model was used to reduce the dimension of image feature vector. Secondly, in order to represent the image by a small number of coefficients, a sparse constraint was added to the NMF model as one of the regular terms. Then, to make the optimized feature have better inter-class differentiation, the clustering property constraint would be another regular term of the NMF model. Finally, through optimizing the model by using gradient descent method, the best feature basis vector and image feature vector could be acquired. The experimental results show that for three image databases, the acquired features extracted by the improved NMF model are more conducive to correct image classification or identification, and the False Accept Rate (FAR) and False Reject Rate (FRR) are reduced to 0.021 and 0.025 respectively.

Key words: Nonnegative Matrix Factorization (NMF), feature extraction, sparse representation, gradient descent method, feature dimension reduction

中图分类号:

TP391.413

贾旭, 孙福明, 李豪杰, 曹玉东. 具有普适性的改进非负矩阵分解图像特征提取方法[J]. 计算机应用, 2018, 38(1): 233-237.

JIA Xu, SUN Fuming, LI Haojie, CAO Yudong. Image feature extraction method based on improved nonnegative matrix factorization with universality[J]. Journal of Computer Applications, 2018, 38(1): 233-237.

参考文献

[1] SHAN H, ZHANG J, KRUGER U. Learning linear representation of sparse partitioning trees based on unsupervised kernel dimension reduction[J]. IEEE Transaction on Cybernetics, 2016, 46(12):3427-3438.
[2] VLASSIS N, MOTOMURA Y, KROSE B. Supervised dimension reduction of intrinsically low-dimensional data[J]. Neural Computation, 2014, 14(1):191-215.
[3] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755):788-791.
[4] HAN H, LIU S J, GAN L. Non-negativity and dependence constrained sparse coding for image classification[J]. Journal of Visual Communication and Image Representation, 2015, 26:247-254.
[5] WEN J H, TIAN Z, LIU X Z, et al. Neighborhood preserving orthogonal PNMF feature extraction for hyperspectral image classification[J]. IEEE Journal of Selected Topic in Applied Earth Observations and Remote Sensing, 2013, 6(2):759-768.
[6] POMPILI F, GILLIS N, ABSIL P A, et al. Two algorithms for orthogonal nonnegative matrix factorization with application to clustering[J]. Neurocomputing, 2014, 141(2):15-25.
[7] KOTSIA I, ZAFEIRIOU S, PITAS I. A novel discriminant non-negative matrix factorization algorithm with applications to facial image characterization problems[J]. IEEE Transactions on Information Forensics and Security, 2007, 2(3):588-595.
[8] ZDUNEK R, PHAN A H, CICHOCKI A. Image classification with nonnegative matrix factorization based on spectral projected gradient[J]. Artificial Neural Networks, 2015, 4:31-50.
[9] JI Z, PANG Y, LI X. Relevance preserving projection and ranking based on one-class classification for Web image[J]. IEEE Transactions on Image Processing, 2015, 24(11):4137-4147.
[10] 汪金涛,曹玉东,孙福明.稀疏约束图正则非负矩阵分解的增量学习算法[J].计算机应用,2017,37(4):1071-1074.(WANG J T, CAO Y D, SUN F M. Incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints[J]. Journal of Computer Applications, 2017, 37(4):1071-1074.)
[11] JIA X, SUN F M, LI H J, et al. Image multi-label annotation based on supervised nonnegative matrix factorization with new matching measurement[J]. Neurocomputing, 2017, 219(C):518-525.
[12] LIU H, WU Z, CAI D, et al. Constrained nonnegative matrix factorization for image representation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(7):1299-1311.
[13] CAI D, HE X, HAN J, et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(8):1548-1560.
[14] LIU Q. Kernel local sparse representation based classifier[J]. Neural Processing Letters, 2016, 60(1):1684-1695.
[15] YIN Y L, LIU L L, SUN X W. SDUMLA-HMT:a multimodal biometric database[C]//Proceedings of the 6th Chinese Conference on Biometric Recognition. Beijing:[s.n.], 2011:260-268.
[16] CHUA T S, TANG J, HONG R, et al. NUSWIDE:a real-world Web image database from National University of Singapore[C]//Proceedings of the 2009 ACM International Conference on Image and Video Retrieval. New York:ACM, 2009:48.
[17] 苑玮琦,王爇,孙书会.基于2DFLD的手背静脉识别算法[J].计算机应用,2010,30(3):646-649.(YUAN W Q, WANG R, SUN S H. Palm-dorsa vein recognition based on two-dimensional Fisher linear discriminant[J]. Journal of Computer Applications, 2010, 30(3):646-649.)
[18] 胡学考,孙福明,李豪杰.基于稀疏约束的半监督非负矩阵分解算法[J].计算机科学,2015,42(7):280-284.(HU X K, SUN F M, LI H J. Constrained nonnegative matrix factorization with sparseness for image representation[J]. Computer Science, 2015, 42(7):280-284.)

具有普适性的改进非负矩阵分解图像特征提取方法

Image feature extraction method based on improved nonnegative matrix factorization with universality

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]	杨鑫, 陈雪妮, 吴春江, 周世杰. 结合变种残差模型和Transformer的城市公路短时交通流预测[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2947-2951.
[2]	付帅, 郭小英, 白茹意, 闫涛, 陈斌. 改进的CloFormer模型与有序回归相结合的年龄评估方法[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2372-2380.
[3]	陈彤, 杨丰玉, 熊宇, 严荭, 邱福星. 基于多尺度频率通道注意力融合的声纹库构建方法[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2407-2413.
[4]	龙伍丹, 彭博, 胡节, 申颖, 丁丹妮. 基于加强特征提取的道路病害检测算法[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2264-2270.
[5]	刘瑞华, 郝子赫, 邹洋杨. 基于多层级精细特征融合的步态识别算法[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2250-2257.
[6]	吴郅昊, 迟子秋, 肖婷, 王喆. 基于元学习自适应的小样本语音合成[J]. 《计算机应用》唯一官方网站, 2024, 44(5): 1629-1635.
[7]	崔晨辉, 蔺素珍, 李大威, 禄晓飞, 武杰. 基于孪生网络和Transformer的红外弱小目标跟踪方法[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 563-571.
[8]	范艺扬, 张洋, 曾尚, 曾渝, 付茂栗. 基于分解和频域特征提取的多变量长时间序列预测模型[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3442-3448.
[9]	赵培, 乔焰, 胡荣耀, 袁新宇, 李敏悦, 张本初. 基于多域特征提取的多变量时间序列异常检测[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3419-3426.
[10]	刘涛, 鞠事宏, 高一萌. 基于改进YOLOv8n的无人机视角下小目标检测算法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3603-3609.
[11]	花晓雨, 李冬芬, 付优, 毕可骏, 应时, 王瑞锦. 结合层次图神经网络与长短期记忆的产业链风险评估预警模型[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3223-3231.
[12]	李牧, 杨宇恒, 柯熙政. 基于混合特征提取与跨模态特征预测融合的情感识别模型[J]. 《计算机应用》唯一官方网站, 2024, 44(1): 86-93.
[13]	张雨宁, 阿布都克力木·阿布力孜, 梅悌胜, 徐春, 麦尔达娜·买买提热依木, 哈里旦木·阿布都克里木, 侯钰涛. 基于自监督特征提取的骨骼X线影像异常检测方法[J]. 《计算机应用》唯一官方网站, 2024, 44(1): 175-181.
[14]	田悦霖, 黄瑞章, 任丽娜. 融合局部语义特征的学者细粒度信息提取方法[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2707-2714.
[15]	王彬, 向甜, 吕艺东, 王晓帆. 基于NSGA‑Ⅱ的自适应多尺度特征通道分组优化算法[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1401-1408.