Journal of KIISE:Computing Practices and Letters (한국정보과학회논문지:컴퓨팅의 실제 및 레터)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Performance Improvement of Image Retrieval System by Presenting Query based on Human Perception

인간의 인지도에 근거한 질의를 통한 영상 검색의 성능 향상

  • 유헌우 (연세대학교 인지과학연구소) ;
  • 장동식 (고려대학교 산업시스템정보공학과) ;
  • 오근태 (수원대학교 산업정보공학과)
  • Published : 2003.04.01
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Abstract

Image similarity is often decided by computing the distance between two feature vectors. Unfortunately, the feature vector cannot always reflect the notion of similarity in human perception. Therefore, most current image retrieval systems use weights measuring the importance of each feature. In this paper new initial weight selection and update rules are proposed for image retrieval purpose. In order to obtain the purpose, database images are first divided into groups based on human perception and, inner and outer query are performed, and, then, optimal feature weights for each database images are computed through searching the group where the result images among retrieved images are belong. Experimental results on 2000 images show the performance of proposed algorithm.

영상간의 유사도는 일반적으로 영상으로부터 추출한 특징벡터간의 벡터공간상의 거리를 계산해서 판단한다. 그러나 이러한 특징벡터가 유사도 계산을 위한 하나의 방법이지만 항상 인간의 유사도 개념을 충실히 반영하지는 않는다. 그러므로 현존하는 대부분의 영상검색시스템들은 각 특징간의 중요도를 선정하여 유사도에 반영하는 방법을 사용하고 있다. 본 논문에서는 영상검색을 위한 새로운 초기 가중치 설정과 갱신 알고리즘을 제안한다. 이를 위해서 먼저 데이터 베이스 영상을 인간의 인지도 판단에 의해 그룹화 한 후, 내부질의와 외부질의를 수행하고, 검색된 영상중 유사한 영상이 어느 그룹에 속하는지 알아내어 각 영상별로 유사도 계산에 필요한 최적 특징 가중치를 계산한다. 2000개의 영상 데이타에 대한 실험을 통해서 제안된 알고리즘의 우수성을 보인다.

Keywords

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