类别约束下的低秩优化特征字典构造方法

doi:10.11772/j.issn.1001-9081.2014.09.2668

计算机应用 ›› 2014, Vol. 34 ›› Issue (9): 2668-2672.DOI: 10.11772/j.issn.1001-9081.2014.09.2668

类别约束下的低秩优化特征字典构造方法

吕煊¹,刘玉淑²,丁洪富¹,李爱迪¹

1. 重庆市国土资源和房屋勘测规划院，重庆 400020；
2. 齐鲁工业大学电气与自动化学院，济南 250353

收稿日期:2014-03-11 修回日期:2014-05-09 出版日期:2014-09-01 发布日期:2014-09-30
通讯作者: 吕煊
作者简介:
吕煊(1982-),男,山东淄博人,工程师,博士,主要研究方向:图像分类、数据挖掘;
刘玉淑(1982-),女,山东淄博人,讲师,博士,主要研究方向:数字图像处理、模式识别;
丁洪富(1974-),男,重庆人,正高级工程师,主要研究方向:地理信息；
李爱迪(1979-)，男，四川人，高级工程师，主要研究方向：地理信息系统。
基金资助:
国土资源部公益性项目

Low-rank optimization characteristic dictionary training approach with category constraint

LYV Xuan¹,LIU Yushu²,DING Hongfu¹,LI Aidi¹

1. Chongqing Land Resources Housing Surveying and Planning Institute, Chongqing 400020, China
2. School of Electrical Engineering and Automation, Qilu University of Technology, Jinan Shandong 250353, China

Received:2014-03-11 Revised:2014-05-09 Online:2014-09-01 Published:2014-09-30
Contact: LYV Xuan

摘要/Abstract

摘要：

字典模型(BOW)是一种经典的图像描述方法，模型中特征字典的构造方法至关重要。针对特征字典构造问题，提出了一种类别约束下的低秩优化特征字典构造方法LRC-DT，通过低秩优化的方法使训练出来的特征字典在描述同类图像时表示系数矩阵的秩相对较低，从而将类别信息引入到字典学习中，提高字典对图像描述的可分辨性。在标准公测库Caltech-101和Caltech-256上的实验结果表明：将SPM、稀疏编码下的SPM(ScSPM)、局部线性编码(LLC)和线性核函数的SPM(LSPM)编码方法中的特征字典替换为加入低秩约束(LRC)的特征字典后，随着训练样本数目增多，字典模型的分类准确率与未引入低秩约束的方法相比有所提高。

Abstract:

Bag Of Words (BOW) is a classical approach of image description, and the method of constructing the characteristic dictionary in this model is very important. A category constrained low-rank optimization characteristic dictionary training approach named LRC-DT was proposed for the characteristic dictionary construction. Through the low-rank optimization, the rank of the coefficient matrix constructed by same category images was minimized. Then the classification information was introduced into the characteristic dictionary learning to improve the identifiability of characteristic dictionary for image description. Some experiments were conducted on two standard image databases including Caltech-101 and Caltech-256, and the characteristic dictionary of SPM (Spatial Pyramid Matching), ScSPM (Sparse codes SPM), LLC (Locality-constrained Linear Coding) and LSPM (Linear SPM) were replaced by constrained low-rank optimization characteristic dictionary. The experimental results show that the proposed method can consistently offer better performance than not employing the category constrained low-rank optimization, its classification accuracy is improved with the increase of the training sample number.

中图分类号:

TP391

吕煊刘玉淑丁洪富李爱迪. 类别约束下的低秩优化特征字典构造方法[J]. 计算机应用, 2014, 34(9): 2668-2672.

LYV Xuan LIU Yushu DING Hongfu LI Aidi. Low-rank optimization characteristic dictionary training approach with category constraint[J]. Journal of Computer Applications, 2014, 34(9): 2668-2672.

参考文献

［1］JEGOU H, DOUZE M, SCHIMID C. Packing bag-of-features ［C］// Proceedings of the IEEE 12th International Conference on Computer Vision. Piscataway: IEEE, 2009: 2357-2364.

［2］PEYRE G. A review of adaptive image representations ［J］. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 896-911.

［3］PHILBIN J, CHUM O, ISARD M, et al.Object retrieval with large vocabularies and fast spatial matching ［C］// Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2007: 1-8.

［4］CHUM O, PHILBIN J, SIVIC J, et al.Total recall: automatic query expansion with a generative feature model for object retrieval ［C］// Proceedings of the IEEE 11th International Conference on Computer Vision. Piscataway: IEEE, 2007: 1-8.

［5］LI F F, PERONA P. A Bayesian hierarchical model for learning natural scene categories［C］// Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2005, 2: 524-531.

［6］DENG C, CAO H. Construction of multiscale ridgelet dictionary and its application for image coding ［J］. Journal of Image and Graphics, 2009, 14(7): 1273-1278. (邓承志,曹汉强.多尺度脊波字典的构造及其在图像编码中的应用［J］.中国图象图形学报,2009,14(7):1273-1278.)

［7］SUN Y, WEI Z, XIAO L, et al.Multimorphology sparsity regularized image super-resolution ［J］. Acta Electronica Sinica, 2010, 38(12): 2898-2903. (孙玉宝,韦志辉,肖亮,等.多形态稀疏性正则化的图像超分辨率算法［J］.电子学报, 2010, 38(12): 2898-2903.)

［8］AHARON M, ELAD M, BRUCKSTEIN A. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation ［J］. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.

［9］CSRUKA G, DANCE C R, FAN L, et al.Visual categorization with bags of keypoints ［C］// ECCV 2004: Proceedings of the 8th European Conference on Computer Vision, LNCS 3024. Berlin: Springer-Verlag, 2004: 1-22.

［10］CSURKA G, DANCE C R, PERRONNIN F, et al.Generic visual categorization using weak geometry ［C］// Toward Category-Level Object Recognition, LNCS 4170. Berlin: Springer-Verlag, 2006: 207-224.

［11］LAZEBNIK S, SCHMID C, PONCE J. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories ［C］// Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2006, 2: 2169-2176.

［12］ZHANG J, MARSZALEK M, LAZEBNIK S, et al.Local features and kernels for classification of texture and object categories: a comprehensive study ［J］. International Journal of Computer Vision, 2007, 73(2): 213-238.

［13］SIVIC J, ZISSERMAN A. Video Google: a text retrieval approach to object matching in videos［C］// Proceedings of the 9th IEEE International Conference on Computer Vision. Washington, DC: IEEE Computer Society, 2002: 1470-1477.

［14］BOIMAN O, SHECHTMAN E, IRANI M. In defense of nearest-neighbor based image classification ［C］// Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2008: 1-8.

［15］LI F F, FERGUS R, PERONA P. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories ［J］. Computer Vision and Image Understanding, 2007, 106(1): 59-70.

［16］BOSCH A, ZISSERMAN A, MUNOZ X. Scene classification using a hybrid generative/ dicriminative approach ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(4): 712-727.

［17］LIU Y, JIN R, SUKTHANKAR R, et al.Unifying discriminative visual codebook generation with classifier training for object category recognition ［C］// Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2008: 1-8.

［18］JURIE F, TRIGGS B. Creating efficient codebooks for visual recognition ［C］// ICCV 2005: Proceedings of the 2005 IEEE International Conference on Computer Vision. Washington, DC: IEEE Computer Society, 2005, 1: 604-610.

［19］BURGHOUTS G J, SCHUTTE K. Spatio-temporal layout of human actions for improved bag-of-words action detection ［J］. Pattern Recognition Letters, 2013, 34(15): 1861-1869.

［20］BANERJI S, SINHA A, LIU C. A new Bag of Words LBP (BoWL) descriptor for scene image classification［C］// CAIP 2013: Proceedings of the 15th International Conference on Computer Analysis of Images and Patterns, LNCS 8047. Berlin: Springer-Verlag, 2013: 490-497.

［21］YANG J, YU K, GONG Y, et al.Linear spatial pyramid matching using sparse coding for image classification ［C］// Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 1794-1801.

［22］WANG J, YANG J, YU K, et al.Locality-constrained linear coding for image classification ［C］// Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2010: 3360-3367.

［23］RUBINSTEIN, R, PELEG T, et al.Analysis K-SVD: a dictionary-learning algorithm for the analysis sparse model ［J］. IEEE Transactions on Signal Processing, 2013, 61(3): 661-677.

［24］JIANG Z, LIN Z, DAVIS L S. Label consistent K-SVD: learning a discriminative dictionary for recognition ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2651-2664.

［25］ZHANG Z, GANESH A, LIANG X, et al.TILT: transform invariant low-rank textures ［J］. International Journal of Computer Vision, 2012, 99(1): 1-24.

［26］LIU G, LIN Z, YAN C, et al.Robust recovery of subspace structures by low-rank representation ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 171-184.

［27］ZHANG N, YANG J. Low-rank representation based discriminative projection for robust feature extraction ［J］. Neurocomputing, 2013, 111: 13-20.

［28］SHALIT U, WEINSHALL D, CHECHIK G. Online learning in the embedded manifold of low-rank matrices ［J］. Journal of Machine Learning Research, 2012, 13(1): 429-458.

［29］LIU Y, JIAO L C, SHANG F, et al.An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion ［J］. Neural Networks, 2013, 48: 8-18.

［30］ZHANG X, SUN F, LIU G, et al.Fast low-rank subspace segmentation ［J］. IEEE Transactions on Knowledge and Data Engineering, 2013, 26(5): 1293-1297.

［31］YANG J, YIN W T, ZHANG Y, et al.A fast algorithm for edge preserving variational multichannel image restoration ［J］. SIAM Journal on Imaging Sciences, 2009, 2(2): 569-592.

［32］BOIMAN O, SHECHTMAN E, IRANI M. In defense of nearest-neighbor based image classification ［C］// CVPR 2008: Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2008: 1-8.

［33］SHOTTON J, WINN J, ROTHER C, et al.Textonboost for image understanding: multi-class object recognition and segmentation by jointly modeling appearance, shape and context ［J］. International Journal of Computer Vision, 2009, 81(1): 2-23.

［34］ZHANG H, BERG A C, MAIRE M, et al.SVM-KNN: discriminative nearest heighbor classification for visual category recognition ［C］// Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2006, 2: 2126-2136.

［35］GRIFFIN G, HOLUB A, PERONA P. Caltech-256 object category dataset, TR 7694 ［R］. Pasadena: California Institute of Technology, 2007.

[1]	吴军欧阳艾嘉张琳. 基于影响度的统计显著序列模式挖掘算法[J]. 计算机应用, 0, (): 0-0.
[2]	张璐方春祝铭. 基于Res2Net-YOLACT和融合特征的室内跌倒检测算法[J]. 计算机应用, 0, (): 0-0.
[3]	殷雨昌王洪元陈莉冯尊登肖宇. 基于单标注样本的多损失学习与联合度量视频行人重识别[J]. 计算机应用, 0, (): 0-0.
[4]	胡军许正康刘立钟福金张清华. 融合多粒度社区信息的网络嵌入方法[J]. 计算机应用, 0, (): 0-0.
[5]	李润泽孙雪姣. 基于时间条件提取序列的数据流偏好查询[J]. 计算机应用, 0, (): 0-0.
[6]	罗圣钦陈金怡李洪均. 基于注意力机制的多尺度残差UNet实现乳腺癌灶分割[J]. 计算机应用, 0, (): 0-0.
[7]	曹一珉蔡磊高敬阳. 基于生成对抗网络的基因数据生成方法[J]. 计算机应用, 0, (): 0-0.
[8]	陈冲闫珠赵继轩何为梁华庆. 基于集合经验模态分解和长短期记忆网络的催化裂化装置NOx排放预测[J]. 计算机应用, 0, (): 0-0.
[9]	徐光柱林文杰陈莎匡婉雷帮军周军. U-Net与自适应阈值脉冲耦合神经网络相结合的眼底血管分割方法[J]. 计算机应用, 0, (): 0-0.
[10]	杨鼎康黄帅王顺利翟鹏李一丹张立华. 基于对抗生成网络和网络集成的面部表情识别方法EE-GAN[J]. 计算机应用, 0, (): 0-0.
[11]	李讷徐光柱雷帮军马国亮石勇涛. 交通道路行驶车辆车标识别算法[J]. 计算机应用, 0, (): 0-0.
[12]	孟杰王莉杨延杰廉飚. 基于多模态深度融合的虚假信息检测[J]. 计算机应用, 0, (): 0-0.
[13]	秦庭威赵鹏程秦品乐曾建朝柴锐黄永琦. 基于残差注意力机制的点云配准算法[J]. 计算机应用, 0, (): 0-0.
[14]	鲁永帅唐英杰马鑫然. 基于深度特征融合的无纺布低对比度浆丝缺陷检测方法[J]. 计算机应用, 0, (): 0-0.
[15]	王宇航周永霞吴良武. 基于高斯函数的池化算法[J]. 计算机应用, 0, (): 0-0.

类别约束下的低秩优化特征字典构造方法

Low-rank optimization characteristic dictionary training approach with category constraint

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