TY - GEN
T1 - Classification of photo and sketch images using convolutional neural networks
AU - Sasaki, Kazuma
AU - Yamakawa, Madoka
AU - Sekiguchi, Kana
AU - Ogata, Tetsuya
N1 - Funding Information:
The work has been supported by MEXT Grant-in-Aid for Scientific Research (A) 15H01710.
Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Content-Based Image Retrieval (CBIR) system enables us to access images using only images as queries, instead of keywords. Photorealistic images, and hand-drawn sketch image can be used as a queries as well. Recently, convolutional neural networks (CNNs) are used to discriminate images including sketches. However, the tasks are limited to classifying only one type of images, either photo or sketch images, due to the lack of a large dataset of sketch images and the large difference of their visual characteristics. In this paper, we introduce a simple way to prepare training datasets, which can enable the CNN model to classify both types of images by color transforming photo and illustration images. Through the training experiment, we show that the proposed method contributes to the improvement of classification accuracy.
AB - Content-Based Image Retrieval (CBIR) system enables us to access images using only images as queries, instead of keywords. Photorealistic images, and hand-drawn sketch image can be used as a queries as well. Recently, convolutional neural networks (CNNs) are used to discriminate images including sketches. However, the tasks are limited to classifying only one type of images, either photo or sketch images, due to the lack of a large dataset of sketch images and the large difference of their visual characteristics. In this paper, we introduce a simple way to prepare training datasets, which can enable the CNN model to classify both types of images by color transforming photo and illustration images. Through the training experiment, we show that the proposed method contributes to the improvement of classification accuracy.
KW - Content based image retrieval
KW - Hand-drawn sketch
UR - http://www.scopus.com/inward/record.url?scp=84988373762&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-44781-0_34
DO - 10.1007/978-3-319-44781-0_34
M3 - Conference contribution
AN - SCOPUS:84988373762
SN - 9783319447803
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 283
EP - 290
BT - Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
A2 - Villa, Alessandro E.P.
A2 - Masulli, Paolo
A2 - Rivero, Antonio Javier Pons
PB - Springer Verlag
T2 - 25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016
Y2 - 6 September 2016 through 9 September 2016
ER -