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.
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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
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DOI: https://doi.org/10.1007/978-3-030-01418-6_23
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