Application of multi-source data fusion of modern dress design with the artistic elements of Chinese embroidery

Abstract

Abstract This paper first investigates the method of extracting features from Chinese embroidery art images, including preprocessing, filtering, and enhancement steps. Then, the multi-classification recognition technique based on an improved support vector machine is used to classify and recognize different modern dress designs. Non-iterative and conjugate orthogonal algorithms are proposed for a feature fusion method based on partial least squares analysis. In the performance analysis part, the performance of feature extraction classification, data fusion energy, and fused data noise are evaluated. Lastly, the impact of the dress style generation map incorporating fused Chinese embroidery elements is analyzed. The study shows that the method in this paper can save 33.4 J of energy consumption after running for 250 s compared with the popular learning-based method. At least a 10% improvement in the number of dress style graphs that interest social event participants, etiquette service practitioners, and performers was achieved after multiple training iterations.