nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

A Novel Image Classification Method with CNN-XGBoost Model

verfasst von : Xudie Ren, Haonan Guo, Shenghong Li, Shilin Wang, Jianhua Li

Erschienen in: Digital Forensics and Watermarking

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Image classification problem is one of most important research directions in image processing and has become the focus of research in many years due to its diversity and complexity of image information. In view of the existing image classification models’ failure to fully utilize the information of images, this paper proposes a novel image classification method of combining the Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost), which are two outstanding classifiers. The presented CNN-XGBoost model provides more precise output by integrating CNN as a trainable feature extractor to automatically obtain features from input and XGBoost as a recognizer in the top level of the network to produce results. Experiments are implemented on the well-known MNIST and CIFAR-10 databases. The results prove that the new method performs better compared with other methods on the same databases, which verify the effectiveness of the proposed method in image classification problem.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Novel Visual Cryptography Scheme with Different Importance of Shadows

Nächstes Kapitel Improvement and Evaluation of Time-Spread Echo Hiding Technology for Long-Distance Voice Evacuation Systems

Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefMATH

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25(2), pp. 1097–1105 (2012)

Erhan, D., Szegedy, C., Toshev, A., et al.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2147–2154 (2014)

Diao, W., Sun, X., Zheng, X., et al.: Efficient saliency-based object detection in remote sensing images using deep belief networks. IEEE Geosci. Remote Sens. Lett. 13(2), 137–141 (2016)CrossRef

Li, C., Wei, W., Wang, J., Tang, W., Zhao, S.: Face recognition based on deep belief network combined with center-symmetric local binary pattern. In: Park, J., Jin, H., Jeong, Y.S., Khan, M. (eds.) Advanced Multimedia and Ubiquitous Engineering. LNEE, vol. 393, pp. 277–283. Springer, Singapore (2016). doi:10.1007/978-981-10-1536-6_37 CrossRef

Taigman, Y., Yang, M., Ranzato, M.A., et al.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)

Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)

Song, R., Chen, S., Deng, B., Li, L.: eXtreme gradient boosting for identifying individual users across different digital devices. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds.) WAIM 2016, Part I. LNCS, vol. 9658, pp. 43–54. Springer, Cham (2016). doi:10.1007/978-3-319-39937-9_4 CrossRef

Mackey, L., Bryan, J., Mo, M.Y.: Weighted classification cascades for optimizing discovery significance in the HIGGSML challenge. In: NIPS 2014 Workshop on High-energy Physics and Machine Learning, pp. 129–134 (2015)

10.

Bekkerman, R.: The present and the future of the KDD cup competition: an outsider’s perspective

11.

Lecun, Y., Boser, B., Denker, J.S., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)

12.

Zipser, D., Andersen, R.A.: A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature 331(6158), 679–684 (1988)CrossRef

13.

Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)

14.

Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef

15.

Chan, T.H., Jia, K., Gao, S., et al.: Pcanet: a simple deep learning baseline for image classification. IEEE Trans. Image Process. 24(12), 5017–5032 (2015)MathSciNetCrossRef

16.

Ao, D.: Integration of Unsupervised Feature Learning and Neural Networks Applied to Image Recognition, pp. 19–37. South China University of Technology (2014)

17.

Elleuch, M., Maalej, R., Kherallah, M.: A new design based-SVM of the CNN classifier architecture with dropout for offline arabic handwritten recognition. Procedia Comput. Sci. 80, 1712–1723 (2016)CrossRef

18.

Le, Q., Sarlós, T., Smola, A.: Fastfood-approximating kernel expansions in loglinear time. In: Proceedings of the International Conference on Machine Learning (2013)

19.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefMATH

20.

Krizhevsky, A.: Learning multiple layers of features from tiny images. Master’s thesis, pp. 30–35. University of Toronto (2009)

Titel: A Novel Image Classification Method with CNN-XGBoost Model
verfasst von: Xudie Ren
Haonan Guo
Shenghong Li
Shilin Wang
Jianhua Li
Verlag: Springer International Publishing
Buch: Digital Forensics and Watermarking
Print ISBN: 978-3-319-64184-3

Electronic ISBN: 978-3-319-64185-0

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-64185-0_28

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"