Collar recognition and matching of clothing style drawings based on complex networks

Abstract

Seeking a fast and effective algorithm for describing and classifying clothing styles is currently a hot topic in the textile and clothing industry. The study first constructs a sample library of collar styles, preprocesses clothing images, and uses complex network models to describe and extract features; Implement clothing image classification based on support vector machines, and finally use image matching algorithms to match the image to be matched with the target object. It was indicated that the complex network model established by the research institute can effectively depict the characteristics of various collar styles, and the complex network model can effectively depict the characteristics of various collar styles. In 10 experiments, the overall average overall accuracy was 98%; Regarding different types of collars, the average accuracy of 10 experiments is above 96%, indicating that the collar classification accuracy based on support vector machine method is very high. The classifying results of the 10 categories obtained by the research method have little change and are relatively stable. For each category, the research method is higher than the other two methods. The results can indicate that the research method has effect and is more suitable for collar recognition and classification. Compared with traditional Hu invariant moments and HOG feature extraction algorithms, the research algorithm has stronger noise suppression ability, and using Euclidean distance as a similarity measure has great advantages.