Abstract
This paper presents an in-depth study and analysis of the image feature extraction technique for ancient ceramic identification using an algorithm of partial differential equations. Image features of ancient ceramics are closely related to specific raw material selection and process technology, and complete acquisition of image features of ancient ceramics is a prerequisite for achieving image feature identification of ancient ceramics, since the quality of extracted area-grown ancient ceramic image feature extraction method is closely related to the background pixels and does not have generalizability. In this paper, we propose a deep learning-based extraction method, using Eased as a deep learning support platform, to extract and validate 5834 images of 272 types of ancient ceramics from kilns, celadon, and Yue kilns after manual labelling and training learning, and the results show that the average complete extraction rate is higher than 99%. The implementation of the deep learning method is summarized and compared with the traditional region growth extraction method, and the results show that the method is robust with the increase of the learning amount and has generalizability, which is a new method to effectively achieve the complete image feature extraction of ancient ceramics. The main content of the finite difference method is to use the ratio of the difference between the function values of two adjacent points and the distance between the two points to approximate the partial derivative of the function with respect to the variable. This idea was used to turn the problem of division into a problem of difference. Recognition of ancient ceramic image features was realized based on the extraction of the overall image features of ancient ceramics, the extraction and recognition of vessel type features, the quantitative recognition of multidimensional feature fusion ornamentation image features, and the implementation of deep learning based on inscription model recognition image feature classification recognition method; three-layer B/S architecture web application system and cross-platform system language called as the architectural support; and database services, deep learning packaging, and digital image processing. The specific implementation method is based on database service, deep learning encapsulation, digital image processing, and third-party invocation, and the service layer fusion and relearning mechanism is proposed to achieve the preliminary intelligent recognition system of ancient ceramic vessel type and ornament image features. The results of the validation test meet the expectation and verify the effectiveness of the ancient ceramic vessel type and ornament image feature recognition system.
Highlights
Ceramics is the collective name for pottery and porcelain, a category of materials and products that have been formed in human production and life for more than 10,000 years [1]
Based on the principles of multivariate statistical analysis methods, several important data analysis techniques are used for data processing, functional design, database construction, and business process design carried out, in addition to the preparatory work required for system coding, as well as the principles and operations of the implementation process of the key modules of the system, and the principles of some nuclear analysis techniques required in the implementation process of the system are introduced in detail to help
It describes in detail the principles of some of the nuclear analysis techniques required in the implementation of the system to help users to recognize the advantages and disadvantages of these techniques
Summary
Ceramics is the collective name for pottery and porcelain, a category of materials and products that have been formed in human production and life for more than 10,000 years [1]. In terms of the prospect and development trend of using data mining and other techniques in the field of ancient ceramic identification, the use of statistical correlation methods to analyse and process a large amount of accumulated ancient ceramic data, and discovering the inner links and patterns implied in the sample data, especially in the classification of broken sources and generations, the use of these methods can be more in-depth for the research work and can achieve better results. This paper proposes an innovative identification method for ancient ceramics supported by artificial intelligence and its auxiliary technology and the initial realization of intelligent identification of ancient ceramics, which provides new methods and routes for future intelligent museums, intelligent identification of ancient ceramics, and value perception of inorganic cultural relics, etc
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