Abstract
In this paper, we propose an artwork retrieval based on similarity of touch using convolutional neural network. In the proposed system, a convolutional neural network is learned so that images can be classified into a group based on a touch, with saturation and value and the histogram of saturation and value as input data, and the trained network is used to realize the retrieval. Using the learned convolution neural network, feature vectors are generated for all images used for learning. The output of the full-connected layer before the soft-max layer when each image is input is obtained and normalized so that the magnitude becomes 1.0 is used as the feature vector. Then, the image and the normalized feature vector corresponding to the image are associated and stored in the database. A retrieval is realized by inputting an image as a retrieval key to the input layer, generating a feature vector, and comparing it with feature vectors in the database. We carried out a series of computer experiments and confirmed that the proposed system can realize artwork retrieval based on similarity of touch with higher accuracy than the conventional system.
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
Mogami, H., Otake, M., Kouno, N., Osana, Y.: Self-organizing map with refractoriness and its application to image retrieval. In: Proceedings of IEEE and INNS International Joint Conference on Neural Networks, Vancouver (2006)
Kawai, H., Osana, Y.: Search accuracy improvement in artwork retrieval based on similarity of touch. In: Proceedings of International Conference, Como (2015)
Ojala, T., Pietiäinen, M., Harwood, D.: A comparative study of texture measures with classification based on distributions. Pattern Recogn. 29(1), 51–59 (1996)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in NIPS, pp. 1097–1105 (2012)
Buckley, C., Voorhees, E.M.: Evaluating evaluation measure stability. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 33–40 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Fujita, T., Osana, Y. (2018). Artwork Retrieval Based on Similarity of Touch Using Convolutional Neural Network. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-01418-6_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01417-9
Online ISBN: 978-3-030-01418-6
eBook Packages: Computer ScienceComputer Science (R0)