Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Research on Improving the Performance of Image based Web Structure Similarity: Combining SSIM and ORB algorithms

  • Received : 2024.09.02
  • Accepted : 2024.10.30
  • Published : 2024.11.29
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Abstract

This study aims to establish a standard to accurately determine the similarity of the results when web pages are generated automatically using AI technology due to the explosive increase in demand for digital business. The YOLO, SSIM, Jaccard, and ORB techniques presented in previous studies related to the existing image similarity evaluation index generally focused on the partial and morphological similarity between the reference and the derived image. However, with the development of more complex and in-depth digital services based on generative AI, the need for comprehensive similarity analysis and determination methods that reflect the context and structure has emerged. Accordingly, this study proposed and verified a method to obtain 'Web Structural Similarity (WSS)' by combining the advantages of SSIM and ORB prior techniques. The research will serve various meaningful implications.

본 연구는 폭증하는 디지털 비즈니스 수요 증가에 따라 AI 기술 등 자동화된 방식으로 웹 페이지가 생성됐을 때, 결과물의 유사도를 정확하게 판별하고자 기준을 수립하고자 한다. 기존의 이미지 유사도 평가지표와 관련된 선행연구에서 제시한 YOLO, FID, Jaccard, SSIM, ORB 기법들은 일반적으로 기준안과 파생된 이미지의 부분적, 형태적 유사도에 집중되어 있었다. 그러나 생성형 AI 기반의 더 복잡하고 심화된 디지털 서비스들의 발전에 따라 맥락과 구조를 반영한 종합적 유사도 분석, 판별방안이 필요함이 대두되었다. 이에 따라 본 연구에서는 SSIM과 ORB 선행기법들의 장점을 조합하여 '웹 구조적 유사도(WSS)'를 구하는 방식을 구상하여 제안 및 검증하였다. 연구결과로 개발된 알고리즘을 통한 생성 이미지 비교평가 시 유의미한 성능 개선에 시사점을 줄 것이다.

Keywords

References

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