基于机器学习的图像识别面向动画人物评论的被动自动化研究

Yutaka Yoshino, Kazuki Nakada, M. Kobayashi, H. Tatsumi
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引用次数: 0

摘要

本研究旨在协助视障人士及动画新手观看动画影片及图像时所遇到的问题。我们重点研究了以下问题:(1)理解行为和情境的困难;(2)区分动画角色的困难;(3)相似动画角色造成的混淆。我们使用深度神经网络来识别动画角色作为初步验证,方法是基于原始动画角色数据库,在一小类数据上从头开始训练自定义卷积神经网络(CNN)。结果表明,在交叉验证中,某些字符组合难以区分。为了解决这个问题,我们基于在自然图像数据库ImageNet上预训练的CNN变体进行了迁移学习。我们确认学习过程平稳,学习曲线渐进式,精度较高。结果表明,在ImageNet上预训练的CNN变体的瓶颈特征对识别动画字符是有效的。
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A Study on Machine Learning-Based Image Identification Towards Assitive Automation of Commentary on Animation Characters
This study aims to assist visually impaired people as well as animation novices by focusing on problems that arise at the time of viewing animation videos and images. We focus on the following problems: (1) difficulty of understanding behaviors and situations, (2) difficulty of discriminating animation characters, and (3) confusion caused by animation characters with similarities. We use deep neural networks to identify animation characters as preliminary verification by training a customized convolutional neural network (CNN) from scratch on a small class of data based on the original database of animation characters. The results show that some combinations of characters are difficult to discriminate in cross validation. To resolve this problem, we performed transfer learning based on the CNN variants pre-trained on the natural image database ImageNet. We confirmed that the learning proceeded steadily with a gradual learning curve, resulting in high accuracy. The results indicate that the bottleneck features of the CNN variants pre-trained on ImageNet are effective in identifying animation characters. Furthermore, we verified the operation speed of the inference of our trained CNN on a microcomputer board with a machine learning accelerator Intel Movidius and confirmed that the speed is sufficient in real-time execution.
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