Digital empowerment:Digitized extraction of patterns of the intangible cultural heritage Miao embroidery based on visual Transformer
Traditional Miao embroidery involves depicting the lines of a basic pattern on paper,cutting the pattern paper tightly onto a cloth backing,and then completing the Miao embroidery using colored threads and a variety of embroidery stitches.Many precious patterns will fade away as the old Miao embroidery breaks down.The digitized extraction of Miao embroidery patterns is not only to reduce the graphic symbols of Miao embroidery into lines,and to reveal the form and function of Miao embroidery's religionized writing,but also to use the extracted Miao embroidery patterns as digital resources for designers'secondary creation.Miao embroidery patterns depend on all kinds of Miao costumes,and manual extraction is greatly restricted.Thanks to the development of computer digital technology,the digitized extraction method can realize the rapid extraction of Miao embroidery patterns.The digitized collection and design reapplication of Miao embroidery patterns can rescue and protect many Miao embroidery patterns that are on the verge of disappearing.In this paper,for the problems in the process of digitized extraction of Miao embroidery patterns,an edge detection method based on progressive sampling(PS)two-stage visual Transformer is proposed to realize the shape extraction of Miao embroidery patterns.The model is based on visual Transformer and is divided into two stages.PS is introduced in both stages to localize important regions to mitigate the loss of structural information inherent in the simple tokensization scheme in visual Transformer.The extracted edges are made to converge to the main part of the Miao embroidery pattern.In the first stage,a global Transformer encoder is used to obtain the global context on coarse-grained patches.Then in the second stage,local Transformer encoders are used to mine local cues at fine-grained patches.Each Transformer encoder is followed by a bi-directional multi-level aggregation decoder for high resolution features.Finally,the globally and locally detected edges are fused by a multi-scale channel attention feature fusion module to obtain better Miao embroidery pattern extraction.In this paper,PS is introduced in both stages to localize the important regions,so that the extracted edges are focused on the main part of the Miao embroidery pattern,and the interference caused by the background of the dress,etc.is reduced.Clearer edges are obtained by weighted fusion of globally and locally detected edges by using the multi-scale channel attention feature fusion module.The experimental results show that the algorithm obtains slimmer lines in the extracted Miao embroidery patterns compared with other algorithms and loses fewer lines of the pattern shape,and the overall effect of the patterns is closest to the labeled image with the best results.The evaluation of this paper's algorithm on the edge detection dataset of Miao embroidery patterns is among the best levels,with an ODS score of 0.848,an OIS score of 0.871,and an AP score of 0.910,an improvement of 0.6％,0.8％,and 0.9％compared to the currently higher-ranked EDTER in the three evaluation indexes of ODS,OIS,and AP,respectively.The digitized extraction and design reapplication of Miao embroidery patterns can rescue and protect many dying Miao embroidery patterns,and can also be widely noticed and valued by the society.The digitally extracted Miao embroidery patterns restore the initial form of Miao embroidery patterns,on which designers can boldly practice and create more novel and interesting works.However,the training of this algorithm model for the dataset of Miao embroidery patterns requires a large number of manually drawn labels in the early stage,which is a heavy workload.In the future,the lightweight processing of the algorithm model can be considered to make the extraction of Miao embroidery patterns more efficient and convenient,and it can also be easily applied to any type of textile pattern extraction.
pattern extractionMiao embroideryintangible cultural heritagevisual Transformerdigitizationedge detection
国家科技期刊平台
NSTL
万方数据
