Abstract
The image of national costumes is the main way of presenting the digitalization of intangible cultural heritage and provides important resources for educational informatization. How to use modern information technology to efficiently retrieve images of national costumes has become a hot research topic. Due to the diverse styles and colorful colors of ethnic costumes, it is difficult to accurately describe and extract visual features. In view of the above problems, this paper proposes an image recognition model of intangible cultural heritage based on CNN and wireless network. First of all, the clothing images of ethnic settlements and ethnic museums are acquired through wireless network transmission, so as to construct an image material library of intangible cultural heritage. Secondly, the CNN algorithm is used to train and optimize the national costume image samples, extract the high-level semantic features of the national costume image and finally realize the efficient retrieval of the national costume image educational resources. This model lays a good foundation for the informatization of national costumes and contributes to the inheritance and protection of intangible cultural heritage.
Highlights
The concept of intangible cultural heritage can be traced back to the "Proposal for the Protection of Civil Creation" issued by UNESCO in 1989 [1]
The experimental results prove that the retrieval accuracy of the convolutional neural network is significantly better than the underlying visual features and the feature extraction method based on support vector machine, particle swarm and BP neural network, which can accurately extract the features of national costumes
A fast and high-accuracy retrieval method is proposed to retrieve the national costume image education resources and provide a way for the digital protection of national culture, which is very meaningful for the inheritance of national culture
Summary
The concept of intangible cultural heritage can be traced back to the "Proposal for the Protection of Civil Creation" issued by UNESCO in 1989 [1]. Convolutional neural networks are formed by stacking multiple different network layers These layers generate corresponding output from input real numbers through some basic mathematical operations and separable functions [24]. 3.2.2 Transpose the convolution layer Sometimes, in order to visualize the characteristics of the network or want to output a larger image through the network, it is usually necessary to use a transposed convolution layer. Unlike general image upsampling operations, transposed convolution does not use a specific interpolation method; as a specific layer of a convolutional neural network, it has learnable parameters [26]. 3.2.4 Fully connected layer After a series of convolution and pooling operations, it is usually necessary to use fully connected layers to integrate previous feature maps to further obtain high-level features of the image [29, 30]. Y is the predicted value of the network. y is the real mark
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