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.
First, the residual blocks are weak in modeling capabilities for global and long-distance dependencies. Second, as the network deepens, the stacking of residual blocks may lead to the loss of resolution and semantic information of the feature maps. To address these issues, this paper proposes an improved ResNet model, as shown in Fig. 7. Considering the model's performance and computational efficiency, this paper selects ResNet34 as the basic architecture for improvement. 12 of 27 AvgPool AvgPool Conv1*1 Conv1*1 Conv1*1 Conv3*3 Conv3*3 Conv3*3 Conv3*3 + + BN+ Input(xa-1) BN BN BN Relu Relu Relu Relu Relu
id: 8cc2c66ba50da629509ef841732cb4d5 - page: 11
Output(xa) Fig. 7. Improved ResNet34 model. Compared with the original ResNet34, the improvements in this paper are mainly reflected in two aspects: (1) In the residual block, the 1*1 convolution is replaced by a combination of average pooling and convolution. The average pooling layer can reduce the resolution of the feature map, thus introducing multi-scale features. At the same time, the introduction of average pooling also increases the sparsity of the network, which helps to improve the model's generalization ability and robustness. (2) After two residual blocks, a long-range residual connection is introduced to add the original input features to the output features of the residual block. This cross-layer feature reuse mechanism can help the network capture global and long-distance dependencies while alleviating the gradient vanishing problem.
id: 9d1a8d02cfadfc2abc2003d1ebfa9ad8 - page: 12
Through the above improvements, the ResNet34 model in this paper can better adapt to the characteristics and requirements of the batik image classification task. On the one hand, the introduction of multi-scale features and the increase of sparsity enable the model to capture the rich texture and detail information in batik images. On the other hand, the addition of long-range residual connections enhances the model's ability to model global semantic information, which helps to distinguish similar batik categories. Fig. 8 shows the overall classification model framework used in this paper, where the input image size is 3*256*256. ButterflyDrumFishFlower 8*8*51216*16*25632*32*12864*64*64128*128*64256*256*3 Bird Fig. 8. Batik image classification model framework. 13 of 27 4. Experiments 4.1. BPKG Construction
id: 9f1810532c624aa3a60963c9dac9c580 - page: 12
4.1.1 Batik Ontology Model Construction Ontology originates from philosophy and is later used in computer science to describe conceptual entities and their relationships. As a key component of the batik knowledge graph, the batik ontology can provide a clear structure and standardized semantics for the construction of the knowledge graph. We adopt the seven-step method to construct the batik ontology model and define four ontology concepts: pattern, meaning, worship consciousness, and prototype source, as shown in Tab. 1. We also define nine relationships based on ontology concepts, such as mean, belong to, worship, origin from, etc. as shown in Tab. 2. Concept Name (Pattern) (Meaning) (Worship Consciousness)
id: aba7a08ff5405c5fd99241cd74e9d611 - page: 13