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- W4387570086 abstract "The strong influence and appeal of TV media and Internet on the audience has greatly strengthened the influence of animation art on politics, economy, society and culture. Image classification is one of the most common tasks in computer vision, which is mainly to extract effective feature information from the original digital image and give the input image a label category. The semi-supervised image classification algorithm can solve the problem of insufficient classification performance under the premise of limited labeled samples. In this article, a movie animation image classification algorithm based on Generative Adversarial Network (GAN) is proposed, which transforms the feature tracking problem of multi-frame movie images into the classification problem of foreground and background of image targets, thus completing the task of classifying, locating, detecting and segmenting movie animation images. The results show that the proposed method can quickly extract the target, and has a fast operation speed, which is enough to realize real-time target tracking. When some labels of training samples are wrong, the classification accuracy of this algorithm will not change much, that is, the fault-tolerant ability of the network will be improved." @default.
- W4387570086 created "2023-10-13" @default.
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- W4387570086 date "2023-01-01" @default.
- W4387570086 modified "2023-10-13" @default.
- W4387570086 title "Research on Image Classification Algorithm of Film and Television Animation Based on Generative Adversarial Network" @default.
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- W4387570086 doi "https://doi.org/10.1007/978-3-031-38651-0_52" @default.
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