A Nonparametric Bayesian Dictionary Learning Algorithm with Clustering Structure Similarity

Daoguang DONG; Guosheng RUI; Wenbiao TIAN; Yang ZHANG; Ge LIU

doi:10.11999/JEIT190496

Volume 42 Issue 11

Nov. 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(11): 2765-2772

Daoguang DONG, Guosheng RUI, Wenbiao TIAN, Yang ZHANG, Ge LIU. A Nonparametric Bayesian Dictionary Learning Algorithm with Clustering Structure Similarity[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2765-2772. doi: 10.11999/JEIT190496

Citation:

Daoguang DONG, Guosheng RUI, Wenbiao TIAN, Yang ZHANG, Ge LIU. A Nonparametric Bayesian Dictionary Learning Algorithm with Clustering Structure Similarity[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2765-2772. doi: 10.11999/JEIT190496

Citation:

PDF( 4811 KB)

A Nonparametric Bayesian Dictionary Learning Algorithm with Clustering Structure Similarity

doi: 10.11999/JEIT190496

Signal and Information Processing Key Laboratory in Shandong, Navy Aviation University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (41606117, 41476089, 61671016)

Received Date: 2019-07-03
Rev Recd Date: 2020-02-28

Available Online: 2020-09-01

Publish Date: 2020-11-16

Abstract

Abstract

Making use of image structure information is a difficult problem in dictionary learning, the traditional nonparametric Bayesian algorithms lack the ability to make full use of image structure information, and faces problem of inefficiency. To this end, a dictionary learning algorithm called Structure Similarity Clustering-Beta Process Factor Analysis (SSC-BPFA) is proposed in this paper, which completes efficient learning of the probabilistic model via variational Bayesian inference and ensures the convergence and self-adaptability of the algorithm. Image denoising and inpainting experiments show that this algorithm has significant advantages in representation accuracy, structure similarity index and running efficiency compared with the existing nonparametric Bayesian dictionary learning algorithms.
- Variational Bayesian,
- Markov Random Field (MRF),
- Dictionary Learning (DL),
- Denoising,
- Inpainting

FullText(HTML)

References(18)

References

XUAN Junyu, LU Jie, ZHANG Guangquan, et al. Doubly nonparametric sparse nonnegative matrix factorization based on dependent Indian buffet processes[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(5): 1835–1849. doi: 10.1109/TNNLS.2017.2676817

LI Shaoyang, TAO Xiaoming, and LU Jianhua. Variational Bayesian inference for nonparametric signal compressive sensing on structured manifolds[C]. 2017 IEEE International Conference on Communications, Paris, France, 2017. doi: 10.1109/ICC.2017.7996389.

DANG H P and CHAINAIS P. Indian buffet process dictionary learning: Algorithms and applications to image processing[J]. International Journal of Approximate Reasoning, 2017, 83: 1–20. doi: 10.1016/j.ijar.2016.12.010

SERRA J G, TESTA M, MOLINA R, et al. Bayesian K-SVD using fast variational inference[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3344–3359. doi: 10.1109/TIP.2017.2681436

NGUYEN V, PHUNG D, BUI H, et al. Discriminative Bayesian nonparametric clustering[C]. The 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017. doi: 10.24963/ijcai.2017/355.

HUYNH V and PHUNG D. Streaming clustering with Bayesian nonparametric models[J]. Neurocomputing, 2017, 258: 52–62. doi: 10.1016/j.neucom.2017.02.078

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

ELAD M and AHARON M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 2006, 15(2): 3736–3745. doi: 10.1109/TIP.2006.881969

ZHOU Mingyuan, CHEN Haojun, PAISLEY J, et al. Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images[J]. IEEE Transactions on Image Processing, 2012, 21(1): 130–144. doi: 10.1109/TIP.2011.2160072

ZHOU Mingyuan, PAISLEY J, and CARIN L. Nonparametric learning of dictionaries for sparse representation of sensor signals[C]. The 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Aruba, Netherlands, 2009. doi: 10.1109/CAMSAP.2009.5413290.

PAISLEY J, ZHOU Mingyuan, SAPIRO G, et al. Nonparametric image interpolation and dictionary learning using spatially-dependent Dirichlet and beta process priors[C]. 2010 IEEE International Conference on Image Processing, Hong Kong, China, 2010. doi: 10.1109/ICIP.2010.5653350.

JU Fujiao, SUN Yanfeng, GAO Junbin, et al. Nonparametric tensor dictionary learning with beta process priors[J]. Neurocomputing, 2016, 218: 120–130. doi: 10.1016/j.neucom.2016.08.064

KNOWLES D and GHAHRAMANI Z. Infinite sparse factor analysis and infinite independent components analysis[C]. The 7th International Conference on Independent Component Analysis and Signal Separation, London, UK, 2007: 381–388. doi: 10.1007/978-3-540-74494-8_48.

ZHANG Linlin, GUINDANI M, VERSACE F, et al. A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses[J]. NeuroImage, 2014, 95: 162–175. doi: 10.1016/j.neuroimage.2014.03.024

AKHTAR N and MIAN A. Nonparametric coupled Bayesian dictionary and classifier learning for hyperspectral classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(9): 4038–4050. doi: 10.1109/TNNLS.2017.2742528

POLATKAN G, ZHOU Mingyuan, CARIN L, et al. A Bayesian nonparametric approach to image super-resolution[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(2): 346–358. doi: 10.1109/TPAMI.2014.2321404

董道广, 芮国胜, 田文飚, 等. 基于结构相似性的非参数贝叶斯字典学习算法[J]. 通信学报, 2019, 40(1): 43–50. doi: 10.11959/j.issn.1000-436x.2019015

DONG Daoguang, RUI Guosheng, TIAN Wenbiao, et al. Nonparametric Bayesian dictionary learning algorithm based on structural similarity[J]. Journal on Communications, 2019, 40(1): 43–50. doi: 10.11959/j.issn.1000-436x.2019015

BISHOP C M. Pattern Recognition and Machine Learning (Information Science and Statistics)[M]. New York: Springer, 2006: 461–522.

Relative Articles

Supplements(0)

Cited By

Proportional views