2:40 PM - 3:00 PM
[1K1-OS-2a-05] Recognizing the Order of Four-scene Comics by Three-Path Convolutional Neural Networks
Keywords:four-scene comics, comics structure, deep convolutional neural network, evolutionary computation
Recently, the comic analysis has become an attractive research topic
in the artificial intelligence fields as comic engineering. In this study, we focused on the four-scene comics and applied deep convolutional neural networks (DCNNs) to those data for understanding the order structure.
We proposed the novel approach for that problem by three-path DCNN with
special input data formats. The hyperparameters of three-path DCNN are
obtained by evolutionary deep learning (evoDL).
The effectiveness of the proposed method is confirmed by computer simulations taking a real four-scene comics structure recognition problem as an example.
in the artificial intelligence fields as comic engineering. In this study, we focused on the four-scene comics and applied deep convolutional neural networks (DCNNs) to those data for understanding the order structure.
We proposed the novel approach for that problem by three-path DCNN with
special input data formats. The hyperparameters of three-path DCNN are
obtained by evolutionary deep learning (evoDL).
The effectiveness of the proposed method is confirmed by computer simulations taking a real four-scene comics structure recognition problem as an example.