Research On Multi-Dimensional Image Processing In Intelligent Design Based On Perceptual Feature Quantification

This paper centers on exploring the utilization of artificial intelligence in the realm of graphic design, with a particular emphasis on the construction of methodological models. Graphic design, as a discipline that amalgamates art and communication, encompasses numerous facets, including visual communication, layout, color theory, and graphic design. In the perpetually evolving technological milieu and burgeoning visual demands, its pivotal role in various domains becomes increasingly conspicuous. Specif-ically, this article investigates a multidimensional image processing technique rooted in perceptual features. Primarily, texture and color characteristics were extracted via con-volutional neural networks (CNN). Subsequently, attention-based fusion convolution was introduced to augment feature performance. Ultimately, by synthesizing multi-tiered fea-tures, intelligent classification of image emotions predicated on perceptual feature vectors was accomplished. The experimental findings evince that the proposed fusion of multi-tiered features for sentiment classification attains an average recognition rate of 85.5% for positive, negative, and neutral emotions. In comparison to single features, fused con-volutional features exhibit superior recognition accuracy. Furthermore, the framework advanced in this article presents a novel feature fusion approach within domains such as image sentiment classification processing, as opposed to prevailing methodologies, thereby providing robust underpinning for prospective endeavors in intelligent graphic design and image sentiment classification. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.