School of film, Xiamen University, Xiamen City, China.
PLoS One. 2023 Jan 26;18(1):e0280848. doi: 10.1371/journal.pone.0280848. eCollection 2023.
With the development of science and technology and the continuous changes of social environment, the development prospect of traditional cinema is worrying. This work aims to improve the publicity effect of movie posters and optimize the marketing efficiency of movie posters and promote the development of film and television industry. First, the design concept of high grossing movie posters is discussed. Then, the concept of movie poster analysis based on Deep Learning (DL) technology is analyzed under Big Data Technology. Finally, a movie poster analysis model is designed based on Convolutional Neural Network (CNN) technology under DL and is evaluated. The results demonstrate that the learning curve of the CNN model reported here is the best in the evaluation of model performance in movie poster analysis. Besides, the learning rate of the model is basically stable when the number of iterations is about 500. The final loss value is around 0.5. Meanwhile, the accuracy rate of the model is also stable at the number of iterations of about 500, and the accuracy rate of the model is around 0.9. In addition, the recognition accuracy of the model designed here in movie poster classification recognition is generally between 60% and 85% in performing theme, style, composition, color scheme, set, and product recognition of movie posters. Moreover, the evaluation of the model in the movie poster style composition suggests that the style composition of movie poster production dramatically varies in different films, in which movie posters focus most on movie product, style, and theme. Compared with other models, the performance of this model is more outstanding in all aspects, which shows that this work has achieved a great technical breakthrough. This work provides a reference for the optimization of the design method of movie posters and contributes to the development of the movie industry.
随着科学技术的发展和社会环境的不断变化，传统电影院的发展前景令人担忧。本工作旨在提高电影海报的宣传效果，优化电影海报的营销效率，促进影视产业的发展。首先，讨论了高票房电影海报的设计理念。然后，在大数据技术下，分析了基于深度学习（DL）技术的电影海报分析概念。最后，基于 DL 下的卷积神经网络（CNN）技术设计了电影海报分析模型，并进行了评估。结果表明，所报道的 CNN 模型的学习曲线在电影海报分析的模型性能评估中是最好的。此外，当迭代次数约为 500 时，模型的学习率基本稳定。最终损失值约为 0.5。同时，当迭代次数约为 500 时，模型的准确率也很稳定，模型的准确率约为 0.9。此外，在执行电影海报主题、风格、构图、配色方案、场景和产品识别时，该模型在电影海报分类识别中的识别准确率一般在 60%到 85%之间。此外，对模型在电影海报风格构图方面的评估表明，不同电影的电影海报制作风格构图差异很大，电影海报主要集中在电影产品、风格和主题上。与其他模型相比，该模型在各个方面的性能都更为突出，这表明本工作在技术上取得了重大突破。本工作为电影海报的设计方法优化提供了参考，为电影产业的发展做出了贡献。
