New Visual Expression of Anime Film Based on Artificial Intelligence and Machine Learning Technology
Author(s) -
Yijie Wan,
Mengqi Ren
Publication year - 2021
Publication title -
journal of sensors
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.399
H-Index - 43
eISSN - 1687-7268
pISSN - 1687-725X
DOI - 10.1155/2021/9945187
Subject(s) - anime , entertainment , animation , expression (computer science) , computer science , film industry , entertainment industry , convolutional neural network , artificial intelligence , hollywood , set (abstract data type) , quality (philosophy) , multimedia , visual arts , computer graphics (images) , art , philosophy , epistemology , art history , movie theater , programming language
With the improvement of material living standards, spiritual entertainment has become more and more important. As a more popular spiritual entertainment project, film and television entertainment is gradually receiving attention from people. However, in recent years, the film industry has developed rapidly, and the output of animation movies has also increased year by year. How to quickly and accurately find the user’s favorite movies in the huge amount of animation movie data has become an urgent problem. Based on the above background, the purpose of this article is to study the new visual expression of animation movies based on artificial intelligence and machine learning technology. This article takes the film industry’s informatization and intelligent development and upgrading as the background, uses computer vision and machine learning technology as the basis to explore new methods and new models for realizing film visual expression, and proposes relevant thinking to promote the innovative development of film visual expression from a strategic level. This article takes the Hollywood anime movie “Kung Fu Panda” as a sample and uses convolutional neural algorithms to study its new visual expression. The study found that after the parameters of the model were determined, the accuracy of the test set did not change much, all around 57%. This is of great significance for improving the audiovisual quality and creative standards of film works and promoting the healthy and sustainable development of the film industry.
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