Research on the Style Classification Method of Clothing Commodity Images

Abstract

E-commerce has a good development trend in commercial digitization. Purchasing clothing products on e-commerce platforms is also the main purchase channel currently selected by consumers. With the development of the times, the public pays more and more attention to the individual pursuit of clothing styles. The current research on the classification of clothing types is relatively complete, but the classification of clothing styles still deserves further research. Based on product style classification, this paper divides 252 suit pictures into two categories: business style and sports style. First, the random walk algorithm segments the foreground of the clothes in the product image from the cluttered background. Then the HOG algorithm is used to extract features from the obtained image data. Finally, the extracted feature vectors are put into the classification model of the support vector machine for binary classification. The result is that the accuracy of style classification after segmentation is better than that without segmentation. It also takes less time to classify after random walk processing.

3D Design Platform of Virtual National Costume Based on Digital Nonlinear Random Matrix

“…It combines the functions of traditional structural materials and functional materials, there is no longer a clear distinction between them, and the functionalization of the structure and the diversification of functions are realized [14]. e characteristics of artificial intelligence contained in smart materials are consistent with the concept of parametric integration of internal relations of objects and self-adaptation of national clothing [15]. With the in-depth research of scholars, intelligent materials will be used by designers in national costume modeling through digital construction.…”