[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

Abstract

The texture image decomposition of porcelain fragments based on convolutional neural network is a functional algorithm based on energy minimization. It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet-based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. The production of big data that meets the characteristics of the background text is actually an image-based normalization method. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. A super-resolution model for certain texture features can improve the restoration effect of such texture images. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg/csv two standardized database datasets after segmentation. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future.

Finding on Machine Learning Approaches for Classification of Ancient Ceramics - A Systematic Literature Review

“…Overall, the various studies demonstrate the technical potential of deep learning in the identification of ancient ceramics, providing assistance in the identification of ancient ceramics, providing assistance in addressing the complex challenges associated with this field. dynasties by digital shape characterization ID2 [23] Selecting the Important Features to Classify the Archaeological Fragments by Using Statistical Tools ID3 [24] Intelligent Dating of Chinese Ancient Ceramics Based on Convolutional Neural Network ID4 [25] Using Deep Learning for the Image Recognition of Motifs on the Center of Sukhothai Ceramics ID5 [20] Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation ID6 [26] Thin section analysis for ceramic petrography using motion analysis and segmentation techniques ID7 [21] Ceramic Decoration Extraction Method Based on Computer Vision and Image Processing ID8 [27] Applications of learning methods to imaging issues in archaeology, regarding ancient ceramics manufacturing ID9 [28] Applications of deep learning to decorated ceramic typology and classification: A case study using Tusayan White Ware from Northeast Arizona ID10 [29] Identification Method of Ancient Ceramics Revision ID11 [30] Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning ID12 [31] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network ID13 [32] Characteristics of Banana Leaf Pattern for Ceramics in Yuan, Ming and Qing Dynasties ID14 [33] Research on ancient ceramics identification by artificial intelligence ID15 [34] A Framework for Design Identification on Heritage Object ID Paper Title Published Year ID16 [35] Using supervised machine learning to classify ceramic fabrics 2018 ID17 [36] Automated classification of archaeological ceramic materials by means of texture measures 2018 ID18 [37] Classification Archaeological Fragments into Groups 2017 ID19 [38] Porcelain image classification based on semi-supervised mean shift clustering 2017 ID20 [39] Machine Vision Based Classification and Identification for Non-destructive Authentication of Ancient Ceramic 2017 ID21 [40] Towards the automatic classification of pottery sherds: two complementary approaches 2015 ID22 [41] Automatic classification of archaeological pottery sherds 2013…”

“…The polarized light conditions, plane-polarized (PL) and cross-polarized (XP), allow the diagnostic features of mineral inclusions to be identified as various other characteristics. ID12 [31] Ancient ceramics in the Ming and Qing Dynasties, especially the year number of the base paragraph on blue and white porcelain, is an important basis for determining the authenticity of the porcelain in this article(ImageNet). ID13 [32] The collection of ancient porcelain images is collected from the collection of museums in China, which is used as the scientific basis and data source for the identification work.…”

“…ImageNet is a well-known open-source dataset that contains a vast collection of labeled images from various categories, including ceramics. Researchers can access and utilize these datasets for their studies, leveraging the available annotations and metadata [31]. The third and final method for researchers that require a dataset is to create their own datasets by collecting data from the internet, including images and related information about ceramics.…”

“…In order to detect lines in 2D images, the Hough transform relates points in the image to sinusoidal curves in an associated Hough space. ID9 [28] Not provided ID10 [29] Not provided ID11 [30] Not provided ID12 [31] The three algorithms are the super-resolution reconstruction algorithm based on interpolation, the super-resolution reconstruction algorithm based on reconstruction, and the algorithm of this article. ID13 [32] The local binary patterns (LBP) and the histogram of oriented gradient (HOG) were used to extract the characteristics of the time from the banana leaf pattern of ancient ceramics images.…”

“…The current results indicate that deep learning with CNNs is a highly accessible and effective method for classifying ceramic fabrics based on images of petrographic thin sections. ID12 [31] The research outputs the structure diagram of the model and saves it for future use. XgBoost is a very good model for structured data learning.…”