Generation of Digital Art Composition Using a Multilabel Learning Algorithm

Abstract

The traditional methods for generating digital art composition have the disadvantage of capturing incomplete geometric information, which leads to obvious defects in the generation results. Therefore, a digital art composition generation method based on the multilabel learning algorithm is proposed in this research. Firstly, a preset series of grids are prepared to generate sampling and fractal pixels on the drawing base. Then, the preset grid construction is constructed by the interactive program of the preset grid library. After the stroke is drawn by the user, the actual motion trajectory of the pen is sampled by the digital panel, and the stroke information in the motion trajectory is obtained by the multilabel learning algorithm. Next, the steps of generating art composition are designed, including generating the skeleton of art composition, generating the geometric network structure of the skeleton, generating the sampling pixel and connecting the fractal pixel, and initializing other attributes of the mesh. Experimental results show that the proposed method has higher sampling rate and geometric information capture rate and has better application performance and prospect.