An automatic generation method of cross-modal fuzzy creativity

• Published in: Journal of intelligent & fuzzy systems Vol. 38; no. 5; pp. 5685 - 5696
• Main Authors: Zhang, Fuquan, Wang, Yiou, Wu, Chensheng
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2020
• Subjects: Creativity; Fuzzy logic; Fuzzy sets; Graph neural networks; Projectiles; Technology transfer
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-179657

Digital creativity is creative expression derived from cultural creativity and information technology. In order to overcome the problem in the creative generation in the condition of fuzzy and uncertain ideas, an automatic generation method of cross-modal fuzzy creativity (AGMCFC) is proposed. In this subject, fuzzy creative data sets and learning retrieval network are constructed for the sake of extracting original creative data effectively. And the logical correlations between creative objects are acquired dynamically based on the graph neural network. Creative objects and creative styles are generated by using generative adversarial nets technology and style transfer technology, respectively. Then, the projectiles, boundary markers and location words of the creative scene objects are generated by analyzing related attributes of each entity. After adjusting the layout, creative works are automatically generated. A fuzzy creative generating environment is implemented. Experimental results show that the screened number of AGMCFC method is about twice as much as that of manual method, and the accuracy rate of AGMCFC method is improved compared with the manual method. AGMCFC method performs well at creative generation of fuzzy ideas automatically.