{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T18:30:57Z","timestamp":1714156257537},"reference-count":24,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,9,15]]},"abstract":" Considering the continuous development of the film industry and the improvement of the living standard among people, movies have gradually come to the civilians. A good movie poster can effectively reflect the content of the movie, attract the audience, stimulate the demand and achieve a good publicity effect. The current movie poster design work is mainly carried out by professional designers, which requires a lot of time and labor cost. In this paper, we propose a context-aware image generation method for assisted design of movie posters using generative adversarial network (named as MPAD-CIP for short). First, the basic information and visual contents of the movie are perceived, and the representative images are extracted, with the use of convolution operations. Then, a backbone network of deep convolutional generative neural network is formulated to generate images for summary of movies. The backbone network is composed of two components: a generator and a discriminator. Their combination realizes the computer-assisted movie poster design by sensing visual context. In the experimental part, the proposed MPAD-CIP method is compared with several benchmark models to demonstrate that the posters generated by this paper are more realistic and versatile, and some of the generated posters are exhibited. <\/jats:p>","DOI":"10.1142\/s021812662350233x","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T03:04:18Z","timestamp":1676603058000},"source":"Crossref","is-referenced-by-count":1,"title":["A Context-Aware Image Generation Method for Assisted Design of Movie Posters Using Generative Adversarial Network"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4601-9888","authenticated-orcid":false,"given":"Yuan","family":"Lu","sequence":"first","affiliation":[{"name":"Zhengzhou Technical College, Zhengshang Road 081#, Zhengzhou 450001, P. R. 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