Analysis of the integration path of contemporary costume culture and clothing design innovation in the context of big data

Abstract

This paper uses feature fusion methods to classify the characteristics of dress culture, focusing on the application scope of pixel-level, feature-level, and decision-level. A system for recognizing dress culture classification is constructed by combining machine vision technology, and the support vector machine algorithm is used to solve the nonlinear mapping problem in two-dimensional space. The range of values of support vectors is established by using decision functions to construct a linear hyperplane. The results show that integrating the color system of costume culture and clothing design can lead to new design paths. 70% of the clothing design works have natural color matching, and 60% have color matching that can alter body shape. The research in this paper provides a new reference path for clothing design innovation.

Keywords

• feature fusion method
• dress culture
• support vector machine
• decision function
• machine vision technology
• 01a13