CMKG: Construction Method of Knowledge Graph for Image Recognition

Lijun Chen, Jingcan Li, Qiuting Cai, Xiangyu Han, Yunqian Ma and Xia Xie ()
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Lijun Chen: School of Cyberspace Security, Hainan University, Haikou 570228, China
Jingcan Li: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Qiuting Cai: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Xiangyu Han: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Yunqian Ma: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Xia Xie: School of Computer Science and Technology, Hainan University, Haikou 570228, China

Mathematics, 2023, vol. 11, issue 19, 1-12

Abstract: With the continuous development of artificial intelligence technology and the exponential growth in the number of images, image detection and recognition technology is becoming more widely used. Image knowledge management is extremely urgent. The data source of a knowledge graph is not only the text and structured data but also the visual or auditory data such as images, video, and audio. How to use multimodal information to build an information management platform is a difficult problem. In this paper, a method is proposed to construct the result of image recognition as a knowledge graph. First of all, based on the improvement in the BlendMASK algorithm, the hollow convolution kernel is added. Secondly, the effect of image recognition and the relationships between all kinds of information are analyzed. Finally, the image knowledge graph is constructed by using the relationship between the image entities. The contributions of this paper are as follows. (1) The hollow convolution kernel is added to reduce the loss from extracting feature information from high-level feature images. (2) In this paper, a method is proposed to determine the relationship between entities by dividing the recognition results of entities in an image with a threshold, which makes it possible for the relationships between images to be interconnected. The experimental results show that this method improves the accuracy and F1 value of the image recognition algorithm. At the same time, the method achieves integrity in the construction of a multimodal knowledge graph.

Keywords: knowledge graph; image recognition; BlendMask; instance segmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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