基于Fisher约束和字典对的图像分类

郭继昌; 张帆; 王楠

doi:10.11999/JEIT160296

基于Fisher约束和字典对的图像分类

doi: 10.11999/JEIT160296

郭继昌^1, ,,
张帆¹,
王楠¹

基金项目:

国家973计划项目(2014CB340400)，天津市自然科学基金(15JCYBJC15500)

计量
- 文章访问数: 1289
- HTML全文浏览量: 139
- PDF下载量: 527
- 被引次数: 0
出版历程
- 收稿日期: 2016-03-31
- 修回日期: 2016-07-25
- 刊出日期: 2017-02-19

Image Classification Based on Fisher Constraint and Dictionary Pair

Funds:

The National 973 Program of China (2014CB340400), The Natural Science Foundation of Tianjin (15JCYBJC15500)

摘要

摘要: 基于稀疏表示的分类方法由于其所具有的简单性和有效性获得了研究者的广泛关注，然而如何建立字典原子与类别信息间的联系仍然是一个重要的问题，与此同时大部分稀疏表示分类方法都需要求解受范数约束的优化问题，使得分类任务的计算较复杂。为解决上述问题，该文提出一种新的基于Fisher约束的字典对学习方法。新方法联合学习结构化综合字典和结构化解析字典，然后通过样本在解析字典上的映射直接求解稀疏系数矩阵；同时采用Fisher判别准则编码系数使系数具有一定的判别性。最后将新方法应用到图像分类中，实验结果表明新方法在提高分类准确率的同时还大大降低了计算复杂度，相较于现有方法具有更好的性能。
- 图像分类 /
- 稀疏表示 /
- 字典对 /
- Fisher约束
Abstract: Classification method based on sparse representation has won wide attention because of its simplicity and effectiveness, while how to adaptively build the relationship between dictionary atoms and class labels is still an important open question, at the same time most of the sparse representation classification methods need to solve a norm constraint optimization problem, which increases the computational complexity in the classification task. To address this issue, this paper proposes a novel Fisher constraint dictionary pair learning method to jointly learn a structured synthesis dictionary and a structured analysis dictionary, then directly obtains the sparse coefficient matrix by analysis dictionary. In this paper, the Fisher criterion is used to encode the coefficients. Finally the new method is applied to image classification task, the experimental results show that the new method not only improves the accuracy of classification but also greatly reduces the computational complexity. Compared with the existing methods, the new method has better performance.
- Image classification /
- Sparse representation /
- Dictionary pair /
- Fisher constraint

HTML全文

参考文献(18)

RUBINSTEIN R, BRUCKSTEIN A, ELAD M, et al. Dictionaries for sparse representation modeling[J]. Proceedings of the IEEE, 2010, 98(6): 1045-1057. doi: 10.1109/JPROC.2010.2040551.

GAO Shenghua, TSANG I, and MA Yi. Learning category- specific dictionary and shared dictionary for fine-grained image categorization[J]. IEEE Transactions on Image Processing, 2014, 23(2): 623-634. doi: 10.1109/TIP.2013. 2290593.

宋相法, 焦李成. 基于稀疏编码和集成学习的多示例多标记图像分类方法[J]. 电子与信息学报, 2013, 35(3): 622-626. doi: 10.3724/SP.J.1146.2012.01218.

SONG Xiangfa and JIAO Licheng. A multi-instance multi-label image classification method based on sparse coding and ensemble learning[J]. Journal of Electronics Information Technology, 2013, 35(3): 622-626. doi: 10.3724/ SP.J.1146.2012.01218.

AHARON M, ELAD M, and BRUCKSTEIN A. K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322. doi: 10.1109/TSP. 2006.881199.

MA Long, WANG Chunheng, XIAO Baihua, et al. Sparse representation for face recognition based on discriminative low-rank dictionary learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 2586-2593.

RAMIREZ I, SPRECHMANN P, SAPIRO G, et al. Classification and clustering via dictionary learning with structured incoherence and shared features[C]. IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010: 3501-3508.

NING Zhou and FAN Jianping. Jointly learning visually correlated dictionaries for large-scale visual recognition applications[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(4): 715-730. doi: 10.1109/ TPAMI.2013.189.

JIANG Zhuolin, LIN Zhe, LARRY S, et al. Label consistent K-SVD: Learning a discriminative dictionary for recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2651-2664. doi: 10.1109/TPAMI. 2013.88.

YANG Meng, ZHANG Lei, FENG Xiangchu, et al. Sparse representation based fisher discrimination dictionary learning for image classication[J]. International Journal of Computer Vision, 2014, 109(3): 209-232. doi: 10.1007/s11263-014- 0722- 8.

练秋生, 石保顺, 陈书贞. 字典学习模型、算法及其应用研究进展[J]. 自动化学报, 2015, 41(2): 240-260. doi: 10.16383/ j.aas.2015.c140252.

LIAN Qiusheng, SHI Baoshun, and CHEN Shuzhen. Research advances on dictionary learning models, algorithms and applications[J]. Acta Automatica Sinica, 2015, 41(2): 240-260. doi: 10.16383/j.aas.2015.c140252.

RUBINSTEIN R, PELEG T, and ELAD M. Analysis K-SVD: A dictionary-learning algorithm for the analysis sparse model[J]. IEEE Transactions on Signal Processing, 2013, 61(3): 661-677. doi: 10.1109/TSP.2012.2226445.

CHEN Yunjin, RANFTL R, and POCK T. Insights into analysis operator learning: From patch-based sparse models to higher order MRFs[J]. IEEE Transactions on Image Processing, 2014, 23(3): 1060-1072. doi: 10.1109/TIP.2014. 2299065.

GU Shuhang, ZHANG Lei, ZUO Wangmeng, et al. Projective dictionary pair learning for pattern classification[C]. Advances in Neural Information Processing System, Vancouver, BC, Canada, 2014, 1: 793-801.

RAKOTOMAMONJY A. Applying alternating direction method of multipliers for constrained dictionary learning[J]. Neurocomputing, 2013, 61(3): 126-136. doi: 10.1016/j. neucom.2012.10.024.

CAI Sijia, ZUO Wangmeng, ZHANG Lei, et al. Support vector guided dictionary learning[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014, 8692: 624-639.

GORSKI J, PFEUFFER F, and KLAMROTH K. Biconvex sets and optimization with biconvex functions: a survey and extensions[J]. Mathematical Methods of Operations Research, 2007, 66(3): 373-407. doi: 10.1007/s00186-007-0161-1.

施引文献

资源附件(0)

访问统计