Journal Home > Volume 6 , Issue 4
Computational Visual Media 2020, 6(4): 467-476 https://doi.org/10.1007/s41095-020-0181-9
Research Article | Open Access | Issue | Published: 14 September 2020

Kernel-blending connection approximated by a neural network for image classification

Show Author's Information Xinxin Liu1,  Yunfeng Zhang1( ),  Fangxun Bao2,  Kai Shao1,  Ziyi Sun1,  Caiming Zhang1,2
Shandong University of Finance and Economics, Jinan 250014, China
Shandong University, Jinan 250100, China
Keywords:
image classification, blending neural network, function approximation, kernel mapping connection, generalizability
Cite this article:
Liu X, Zhang Y, Bao F, et al. Kernel-blending connection approximated by a neural network for image classification. Computational Visual Media, 2020, 6(4): 467-476. https://doi.org/10.1007/s41095-020-0181-9
Download citation
EndNote(RIS)
BibTeX

664

Views

45

Downloads

Citations

15

Crossref

N/A

WoS

16

Scopus

2

CSCD

Abstract Full text About this article

This paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classification. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature classification through neural network learning. First, a feature extractor learns features from the raw images. Next, an automatically constructed kernel mapping connection maps the feature vectors into a feature space. Finally, a linear classifier is used as an output layer of the neural network to provide classification results. Furthermore, a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network. Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.


menu
Abstract
Full text
Outline
About this article

Kernel-blending connection approximated by a neural network for image classification

Show Author's information Xinxin Liu1, Yunfeng Zhang1( ), Fangxun Bao2, Kai Shao1, Ziyi Sun1, Caiming Zhang1,2
Shandong University of Finance and Economics, Jinan 250014, China
Shandong University, Jinan 250100, China

Abstract

This paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classification. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature classification through neural network learning. First, a feature extractor learns features from the raw images. Next, an automatically constructed kernel mapping connection maps the feature vectors into a feature space. Finally, a linear classifier is used as an output layer of the neural network to provide classification results. Furthermore, a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network. Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.

Keywords: image classification, blending neural network, function approximation, kernel mapping connection, generalizability

References(33)

[1]
C. Cortes,; V. Vapnik, Support-vector networks. Machine Learning Vol. 20, 273-297, 1995.
Google Scholar
[2]
E. Bagarinao,; T. Kurita,; M. Higashikubo,; H. Inayoshi, Adapting SVM image classifiers to changes in imaging conditions using incremental SVM: An application to car detection. In: Computer Vision-ACCV 2009. Lecture Notes in Computer Science, Vol. 5996. H. Zha,; R. Taniguchi,; S. Maybank, Eds. Springer Berlin Heidelberg, 363-372, 2010.
[3]
Y. Q. Guo,; X. P. Jia,; D. Paull, Effective sequential classifier training for SVM-based multitemporal remote sensing image classification. arXiv preprint arXiv:1706.04719, 2017.
[4]
G. E. Hinton,; S. Osindero,; Y. W. Teh, A fast learning algorithm for deep belief nets. Neural Computation Vol. 18, No. 7, 1527-1554, 2006.
Google Scholar
[5]
Y. Bengio,; A. Courville,; P. Vincent, Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 35, No. 8, 1798-1828, 2013.
Google Scholar
[6]
Y. LeCun,; B. E. Boser,; J. S. Denker,; D. Henderson,; R. Howard,; W. Hubbard,; L. D. Jackel, Back propagation applied to handwritten zip code recognition. Neural Computation Vol. 1, No. 4, 541-551, 1989.
Google Scholar
[7]
A. Eitel,; J. T. Springenberg,; L. Spinello,; M. Riedmiller,; W. Burgard, Multimodal deep learning for robust RGB-D object recognition. arXiv preprint arXiv:1507.06821, 2015.
[8]
W. W. Shi,; Y. H. Gong,; X. Y. Tao,; D. Cheng,; N. N. Zheng, Fine-grained image classification using modified DCNNs trained by cascaded softmax and generalized large-margin losses IEEE Transactions on Neural Networks and Learning Systems Vol. 30, No. 3, 683-694, 2018.
Google Scholar
[9]
X. X. Niu,; C. Y. Suen, A novel hybrid CNN-SVM classifier for recognizing handwritten digits Pattern Recognition Vol. 45, No. 4, 1318-1325, 2012.
Google Scholar
[10]
X. Sun,; J. Park,; K. Kang,; J. Hur Novel hybrid CNN-SVM model for recognition of functional magnetic resonance images. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 1001-1006, 2017.
[11]
D. H. Hubel,; T. N. Wiesel, Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology Vol. 195, No. 1, 215-243, 1968.
Google Scholar
[12]
K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics Vol. 36, No. 4, 193-202, 1980.
Google Scholar
[13]
M. D. Zeiler,; R. Fergus, Visualizing and understanding convolutional networks. In: Computer Vision - ECCV 2014. Lecture Notes in Computer Science, Vol. 8689. D. Fleet,; T. Pajdla,; B. Schiele,; T. Tuytelaars, Eds. Springer Cham, 818-833, 2014.
[14]
P. Sermanet,; D. Eigen,; X. Zhang,; M. Mathieu,; R. Fergus,; Y. LeCun, OverFeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013.
[15]
F. L. Zhang,; X. Wu,; R.-L. Li,; J. Wang,; Z. H. Zheng,; S. M. Hu, Detecting and removing visual distractors for video aesthetic enhancement. IEEE Transactions on Multimedia Vol. 20, No. 8, 1987-1999, 2018.
Google Scholar
[16]
Y. H. Wen,; L. Gao,; H. B. Fu,; F. L. Zhang,; S. H. Xia, Graph CNNs with motif and variable temporal block for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33, 8989-8996, 2019.
[17]
S. Ioffe,; C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
[18]
M. Lin,; Q. Chen,; S. C. Yan, Network in network. arXiv preprint arXiv:1312.4400, 2013.
[19]
G. Cybenko, Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems Vol. 2, No. 4, 303-314, 1989.
Google Scholar
[20]
Y. LeCun,; L. Bottou,; Y. Bengio,; P. Haffner, Gradient-based learning applied to document recognition. Proceedings of the IEEE Vol. 86, No. 11, 2278-2324, 1998.
Google Scholar
[21]
A. Krizhevsky,; G. Hinton, Learning multiple layers of features from tiny images. Master Thesis. University of Toronto, 2009.
[22]
Y. Tang, Deep learning using support vector machines. arXiv preprint arXiv:1306.0239, 2015.
[23]
W. T. Wan,; Y. Y. Zhong,; T. P. Li,; J. S. Chen, Rethinking feature distribution for loss functions in image classification. arXiv preprint arXiv:1803.02988, 2018.
[24]
H. Lee,; R. Grosse,; R. Ranganath,; A. Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, 609-616, 2009.
[25]
T. H. Chan,; K. Jia,; S. H. Gao,; J. W. Lu,; Z. N. Zeng,; Y. Ma, PCANet: A simple deep learning baseline for image classification? IEEE Transactions on Image Processing Vol. 24, No. 12, 5017-5032, 2015.
Google Scholar
[26]
E. Hosseini-Asl,; J. M. Zurada,; O. Nasraoui, Deep learning of part-based representation of data using sparse autoencoders with nonnegativity constraints. IEEE Transactions on Neural Networks and Learning Systems Vol. 27, No. 12, 2486-2498, 2016.
Google Scholar
[27]
H. Bristow,; A. Eriksson,; S. Lucey, Fast convolutional sparse coding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 391-398, 2013.
[28]
C. Y. Xu,; C. Y. Lu,; X. D. Liang,; J. B. Gao,; W. Zheng,; T. J. Wang,; S. C. Yan, Multi-loss regularized deep neural network. IEEE Transactions on Circuits and Systems for Video Technology Vol. 26, No. 12, 2273-2283, 2016.
Google Scholar
[29]
I. J. Goodfellow,; D. Warde-Farley,; M. Mirza, A. Courville,; Y. Bengio, Maxout networks. arXiv preprint arXiv:1302.4389, 2013.
[30]
L. Wan,; M. Zeiler,; S. Zhang,; Y. LeCun,; R. Fergus, Regularization of neural networks using dropconnect. In: Proceedings of the 30th International Conference on Machine Learning, Vol. 28, 1058-1066, 2013.
[31]
M. Malinowski,; M. Fritz, Learnable pooling regions for image classification. arXiv preprint arXiv:1301.3516, 2013.
[32]
M. D. Zeiler,; R. Fergus,Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv:1301.3557, 2013.
[33]
K. He,; X. Zhang,; S. Ren,; J. Sun, Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778, 2016.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 31 March 2020
Accepted: 18 May 2020
Published: 14 September 2020
Issue date: December 2020

Copyright

© The Author(s) 2020

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61972227 and 61672018), the Natural Science Foundation of Shandong Province (Grant No. ZR2019MF051), the Primary Research and Develop-ment Plan of Shandong Province (Grant No. 2018GGX101013), and the Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions.

Rights and permissions

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.

Return