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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Deep Transfer Learning for Art Classification Problems

verfasst von : Matthia Sabatelli, Mike Kestemont, Walter Daelemans, Pierre Geurts

Erschienen in: Computer Vision – ECCV 2018 Workshops

Verlag: Springer International Publishing

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Abstract

In this paper we investigate whether Deep Convolutional Neural Networks (DCNNs), which have obtained state of the art results on the ImageNet challenge, are able to perform equally well on three different art classification problems. In particular, we assess whether it is beneficial to fine tune the networks instead of just using them as off the shelf feature extractors for a separately trained softmax classifier. Our experiments show how the first approach yields significantly better results and allows the DCNNs to develop new selective attention mechanisms over the images, which provide powerful insights about which pixel regions allow the networks successfully tackle the proposed classification challenges. Furthermore, we also show how DCNNs, which have been fine tuned on a large artistic collection, outperform the same architectures which are pre-trained on the ImageNet dataset only, when it comes to the classification of heritage objects from a different dataset.

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Vorheriges Kapitel Saliency-Driven Variational Retargeting for Historical Maps
Nächstes Kapitel Reflecting on How Artworks Are Processed and Analyzed by Computer Vision
Fußnoten
4
Please note how instead of a softmax layer any kind of machine learning classifier can be used instead. We experimented with both Support Vector Machines (SVMs) and Random Forests but since the results did not significantly differ between classifiers we decided to not include them here.
 
5
To show these results we have used the VGG19 architecture since it provided a better integration with the publicly available source code of the algorithm which can be found at https://​github.​com/​raghakot/​keras-vis.
 
Literatur
1.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)
2.
Ackermann, S., Schawinksi, K., Zhang, C., Weigel, A.K., Turp, M.D.: Using transfer learning to detect galaxy mergers. Mon. Not. Roy. Astronom. Soc. 479, 415–425 (2018)CrossRef
3.
Allen, N.: Collaboration through the colorado digitization project. First Monday 5(6) (2000)
4.
Bidoia, F., Sabatelli, M., Shantia, A., Wiering, M.A., Schomaker, L.: A deep convolutional neural network for location recognition and geometry based information. In: Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018, 16–18 January 2018, Funchal, Madeira, Portugal, pp. 27–36 (2018)
5.
6.
Caruana, R., Lawrence, S., Giles, C.L.: Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Advances in Neural Information Processing Systems, pp. 402–408 (2001)
7.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. arXiv preprint (2016)
8.
9.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)
10.
Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)
11.
12.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
13.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
14.
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2017)
15.
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst (2007)
16.
17.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
18.
Ma, L., Lu, Z., Shang, L., Li, H.: Multimodal convolutional neural networks for matching image and sentence. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2623–2631 (2015)
19.
20.
Mensink, T., Van Gemert, J.: The rijksmuseum challenge: museum-centered visual recognition. In: Proceedings of International Conference on Multimedia Retrieval, p. 451. ACM (2014)
21.
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010)CrossRef
22.
Parry, R.: Digital heritage and the rise of theory in museum computing. In: Museum Management and Curatorship, pp. 333–348 (2005)CrossRef
23.
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 512–519. IEEE (2014)
24.
Reyes, A.K., Caicedo, J.C., Camargo, J.E.: Fine-tuning deep convolutional networks for plant recognition. In: CLEF (Working Notes) (2015)
25.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2014)
26.
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1453–1460. IEEE (2011)
27.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
28.
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35, 1299–1312 (2016)CrossRef
29.
Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. In: COURSERA: Neural Networks for Machine Learning, pp. 26–31 (2012)
30.
Tomè, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M., Tubaro, S.: Deep convolutional neural networks for pedestrian detection. Sig. Process.: Image Commun. 47, 482–489 (2016)
31.
Weibel, S., Kunze, J., Lagoze, C., Wolf, M.: Dublin core metadata for resource discovery. Technical report (1998)
32.
van de Wolfshaar, J., Karaaba, M.F., Wiering, M.A.: Deep convolutional neural networks and support vector machines for gender recognition. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 188–195. IEEE (2015)
33.
Wollheim, R.: On Art and the Mind. Essays and Lectures. Allen Lane, London (1972)
34.
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987–5995. IEEE (2017)
Metadaten
Titel
Deep Transfer Learning for Art Classification Problems
verfasst von
Matthia Sabatelli
Mike Kestemont
Walter Daelemans
Pierre Geurts
Copyright-Jahr
2019
Buch
Computer Vision – ECCV 2018 Workshops

Print ISBN: 978-3-030-11011-6

Electronic ISBN: 978-3-030-11012-3

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-11012-3_48