Research on National Pattern Reuse Design and Optimization Method Based on Improved Shape Grammar

Ning, Ding; Lv, Jian; Chen, Yujia

doi:10.2991/ijcis.d.200310.003

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Hu et al [11] proposed a generative design method for batik patterns based on shape grammars. Considering the low abstraction level of traditional shape grammars in batik design, DING et al [12] improved it based on comprehensive predicate grammar encoding and used particle swarm optimization to optimize the parameters of predicate shape grammars, enhancing the diversity and artistry of batik pattern generation.…”

Section: Introductionmentioning

confidence: 99%

Protection of Guizhou Miao Batik Culture Based on Knowledge Graph and Deep Learning

Quan,

Li,

Liu

et al. 2024

Preprint

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In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their subjects can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 99.0%, 99.0%, 98.9%, and 99.0%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection.

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Section: Introductionmentioning

confidence: 99%

Protection of Guizhou Miao Batik Culture Based on Knowledge Graph and Deep Learning

Quan,

Li,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Considering Fig. 1 The production process of batik the low abstraction level of traditional shape grammars in batik design, DING et al [12] improved it based on comprehensive predicate grammar encoding and used particle swarm optimization to optimize the parameters of predicate shape grammars, enhancing the diversity and artistry of batik pattern generation.…”

Section: Introductionmentioning

confidence: 99%

Protection of Guizhou Miao batik culture based on knowledge graph and deep learning

Quan,

Li,

Liu

et al. 2024

Herit Sci

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In the globalization trend, China’s cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection.

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“…Batik is a traditional art practiced by minorities of Southwest China. Yunnan Miao Batik is characterized by unique handicraft techniques and natural patterns [2]. In Malaysia, sarong clothing demonstrates a person's ethnic origin.…”

Section: Introductionmentioning

confidence: 99%

Color Analysis of Batik Fabric by Facile Smartphone Colorimetry

Sirisathitkul

Kaewareelap

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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The color of a textile material is the first attribute and key component considered by consumers while purchasing cloth. However, measurement of fabric color is a very challenging task in the textile fabrication process. Colorimeters aids this measurement by adding objective assessments to a generally subjective process. The colorimeter application in Android smartphones provides a simple alternative to dedicated colorimetric devices. The purpose of this study was to determine the suitability of the smartphone colorimetry application for a Batik fabric color measurement. The colors of various Batik fabric images in the International Commission on Illumination L*a*b* color space, which includes all the colors that are visible by the human eye, obtained by a spectrophotometer and the colorimeter application were compared. Data of Batik fabric images acquired at three different distances of 10, 20, and 30 cm were analyzed. The color differences between the colorimeter and spectrophotometer results are various depending on the distance from the target. The ΔE * ab and ΔE * ch metrics were used to evaluate the color differences between the reference and sample fabric colors. The lowest mean of ΔE * ab values was 12.11  5.29 measured 20 cm away from each fabric. The mean values of ΔE * ab between pairs of color symbols from the Colorimeter application were comparable to those obtained by the spectrophotometer. The ΔE * ab values were more suitable for fabric color measurement than ΔE * ch. The results indicate that smartphone colorimetry provides reasonable accuracy, is simple to use for amateurs, suitable for fabric color matching, and can satisfy fabric market requirements.

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Research on National Pattern Reuse Design and Optimization Method Based on Improved Shape Grammar

Cited by 5 publications

References 17 publications

Protection of Guizhou Miao Batik Culture Based on Knowledge Graph and Deep Learning

Protection of Guizhou Miao Batik Culture Based on Knowledge Graph and Deep Learning

Protection of Guizhou Miao batik culture based on knowledge graph and deep learning

Color Analysis of Batik Fabric by Facile Smartphone Colorimetry

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