Mathematical Model Design of the Traditional Dress Recognition Algorithm Based on Digital Watermarking Technology

Yue Wang, Wei Li, Yaojun Zhang and Gengxin Sun

Mathematical Problems in Engineering, 2022, vol. 2022, 1-9

Abstract: According to the characteristics of traditional clothing, clothing identification is studied, and clothing identification and clothing culture learning are effectively combined to find a new method for the inheritance of national culture and strive to make contributions to the inheritance of national culture. According to the requirement of the traditional garment identification watermark monitoring system, a self-synchronous digital watermarking algorithm is designed and implemented. Watermark is embedded in the time domain, and feature information is extracted from traditional clothing by means of mean filtering and replaced by watermark to achieve the purpose of embedding information. Blind detection is realized without the participation of the original image. The difference between the traditional costume embedded with watermark and the original traditional costume is almost imperceptible. It can effectively resist synchronous attacks including clipping and time shifting, showing good robustness. Imperceptibility and robustness can be adjusted freely by embedding strength. The HOG + SVM algorithm is applied to minority clothing classification and recognition. By comparing different classifiers, it is concluded that the classifier trained by the support vector machine algorithm has the best classification effect on ethnic clothing. In order to improve the classification effect, the classical algorithm of color, texture, and shape feature extraction was combined with SVM to conduct experiments on the clothing database collected and sorted out in Yunnan ethnic minority communities, and finally, we verified that the HOG feature combined with the SVM classification algorithm achieved good results in the classification of ethnic clothing.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5230996

DOI: 10.1155/2022/5230996

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