Abstract
The problem of classifying images into different predefined categories is an important high-level vision problem. In recent years, convolutional neural networks (CNNs) have been the most popular tool for image classification tasks. CNNs are multi-layered neural networks that can handle complex classification tasks if trained properly. However, training a CNN requires a huge number of labeled images that are not always available for all problem domains. A CNN pre-trained on a different image dataset may not be effective for classification across domains. In this paper, we explore the use of pre-trained CNN not as a classification tool but as a feature extraction tool for painting classification. We run an extensive array of experiments to identify the layers that work best with the problems of artist and style classification, and also discuss several novel representation and classification techniques using these features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)
Banerji, S., Sinha, A., Liu, C.: New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing 117, 173–185 (2013). http://www.sciencedirect.com/science/article/pii/S0925231213001987
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: a deep convolutional activation feature for generic visual recognition. CoRR abs/1310.1531 (2013). http://arxiv.org/abs/1310.1531
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, Cambridge (1990)
Khan, F.S., Beigpour, S., de Weijer, J.V., Felsberg, M.: Painting-91: a large scale database for computational painting categorization. Mach. Vis. Appl. (MVAP) 25(6), 1385–1397 (2014). http://cat.uab.es/joost/painting91
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)
Liu, C., Wechsler, H.: Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. Image Process. 9(1), 132–137 (2000)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, November 2015, (to appear)
Mousavian, A., Kosecka, J.: Deep convolutional features for image based retrieval and scene categorization. CoRR abs/1509.06033 (2015). http://arxiv.org/abs/1509.06033
Puthenputhussery, A., Liu, Q., Liu, C.: Color multi-fusion fisher vector feature for fine art painting categorization and influence analysis. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–9, March 2016
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. CoRR abs/1403.6382 (2014). http://arxiv.org/abs/1403.6382
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. CoRR abs/1312.6229 (2013). http://arxiv.org/abs/1312.6229
Sinha, A., Banerji, S., Liu, C.: Novel color Gabor-LBP-PHOG (GLP) descriptors for object and scene image classification. In: ICVGIP, p. 58 (2012)
Vapnik, Y.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995). doi:10.1007/978-1-4757-3264-1
Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., Torralba, A.: Object detectors emerge in deep scene CNNs. CoRR abs/1412.6856 (2014). http://arxiv.org/abs/1412.6856
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Banerji, S., Sinha, A. (2017). Painting Classification Using a Pre-trained Convolutional Neural Network. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-68124-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68123-8
Online ISBN: 978-3-319-68124-5
eBook Packages: Computer ScienceComputer Science (R0)