Research on Mushroom Image Classification Algorithm Based on Deep Sparse Dictionary Learning

Authors

  • Xiyang Guo

DOI:

https://doi.org/10.54097/1f3xnx82

Keywords:

Dictionary learning; Sparse representation; Autoencoder; Feature extraction.

Abstract

The traditional mushroom feature extraction method has low classification efficiency and unsatisfactory effect. Dictionary learning is widely used in image classification. However, the previous work is to learn dictionaries in the original space, which limits the performance of sparse representation classification. In order to solve the problem of spatial redundancy in traditional convolutional neural networks and the weak performance of deep learning in small samples, an improved dictionary learning algorithm, Deep Sparse Dictionary learning (DSDL), is proposed. The input to DSDL is not a matrix gathered from the original grayscale image or a hand-created feature, but rather a relatively deeper feature extraction via a stack autoencoder. Then, a structured dictionary is designed to reconstruct the deep features according to different categories of distinguishing features. In addition, it is necessary to learn the associated structured projection sparse dictionary to ensure that the decoder updates in the direction of the deconvolution operator error is minimal. By utilizing sparse dictionary learning loss functions and autoencoder loss functions, DSDL can simultaneously learn deep latent features and corresponding dictionary pairs. In the testing phase of DSDL, the minimum errors of deep feature and structured projection components for different classes can be directly represented by basic matrix multiplication operations. Experimental results show that the proposed method achieves a good classification effect on mushroom images, which shows the effectiveness of the method.

Downloads

Download data is not yet available.

References

Chen Y, Kalantidis Y, Li J, et al. Multi-Fiber Networks for Video Recognition [J]. 2018 (1): 364-380.

Unnikrishnan P, Govindan V K, Kumar S D M . Enhanced sparse representation classifier for text classification[J]. Expert Systems with Application, 2019, 129(SEP.):260-272.

L.Shen, G.Sun, Q.Huang, S.Wang, Z.Lin, E.Wu.Multi-level discriminative dictionary learning with application to large scale image classification[J]. IEEE Transactions on Image Processing, 2015, 24(10): 3109-3123.

S.Bahrampour, N.M.Nasrabadi, A.Ray, W.K.Jenkins. Multimodal task-driven dictionary learning for image classification[J]. IEEE transactions on Image Processing,2015, 25(1):24-38.

Mairal, F. Bach, J. Poncd, G. Sapior. Online dictionary learning for sparse coding[C]. Proceedings of the 26th annual international conference on machine learninb. 2009:689-696.

H.Zhang, Liu H, Song and R.F.Sun. Nonlinear dictionary learning based deep neural networks[C]. 2016 International Joint Conference on Neural Networks (IJCNN). IEEE,2016: 3771-3776.

S.Tariyal, H. Aggarwal, A.Majumdar. Greedy deep dictionary learning for hyperspectral image classification [C].2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2016: 1-4.

V.Papyan, Y.Romano, J.Sulam, M.Elad. Convolutionaldictionary learning via local processing[C]. Proceedings of the IEEE International Conference onComputer Vision. 2017: 5296-5304.

J.Yang, M.H.Yang. Top-down visual saliency via jointCRF and dictionary leaming[J]. IEEE transactions onpattern analysis and machine intelligence, 2016, 39(3):576-588.

V.Singhal, A.Majumdar. Majorization minimization technique for optimally solving deep dictionary learning[J]. Neural Processing Letters, 2018, 47(3): 799-814.

Y.Liu, Q.Chen, W.Chen, I. Wassell. Dictionary learning inspired deep network for scene recognition[C]. Thirty-Second AAAI Conference on Artificial Intelligence. 2018.

J.Sulam, V.Papyan, Y.Romano, M.Elad. Multilayer convolutional sparse modeling and dictionary learning[J]. IEEE Transactions on Signal Processing, 2018,66(15): 4090-4104.

J. Hu, Y.P. Tan. Nonlinear dictionary learning with application to image classification[J]. Pattern Recognition, 2018, 75:282-291.

H.Tang, H.Wei, W.Xiao.Deep Micro-Dictionary Learning and Coding Network[C].2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019: 386-395.

Engan K, Aase S O, Husoy J H. Frame based signal compression using method of optimal directions (MOD)[C]//ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No. 99CH36349). IEEE, 1999, 4: 1-4.

Mairal J, Bach F, Ponce J, et al. Online dictionary learning for sparse coding[C]//Proceedings of the 26th annual international conference on machine learning. ACM, 2009: 689-696.

Aharon M, Elad M, Bruckstein A. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.

Krizhevsky A, SutskeverI, Hinton G(2012)Imagenet classification with deep convolutional neural networks.

Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227.

Elhamifar E, Vidal R (2011) Robust classification using structured sparse representation.

Michael G, Stephen B (2013) Cvx: Matlab software for disciplined convex programming, version 2.0 beta.

Zhang, L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition.

Jiang Z, Lin Z, Davis LS (2013) Label consistent k-svd: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell 35(11):2651–2664.

Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation.

Cai S, Zuo W, Zhang L, Feng X, Wang P (2014) Support vector guided dictionary learning.

Gu S, Zhang L, Zuo W, Feng X (2014) Projective dictionary pair learning for pattern classification.

Wang Y, Du H, Zhang Y, Zhang Y (2021) Efficient and robust discriminant dictionary pair learning for pattern classification. Digital Signal Process 118:103227.

Zhang Z et al (2021) Twin-incoherent self-expressive localityadaptive latent dictionary pair learning for classification. IEEE Trans Neural Netw Learn Syst 323:947–961.

Dong J, Yang L, Liu C, Cheng W, Wang W (2022) Support vector machine embedding discriminative dictionary pair learning for pattern classification. Neural Netw 155:498–511.

Ji P, Zhang T, Li H, Salzmann M, Reid I (2017) Deep subspace clustering networks.

Zhang J, et al. (2019) Self-supervised convolutional subspace clustering network, 5468-5477.

Downloads

Published

20-01-2024

Issue

Section

Articles

How to Cite

Research on Mushroom Image Classification Algorithm Based on Deep Sparse Dictionary Learning. (2024). Academic Journal of Science and Technology, 9(1), 235-240. https://doi.org/10.54097/1f3xnx82

Similar Articles

1-10 of 127

You may also start an advanced similarity search for this article.