Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Fast Multi-Resolution Exhaustive Search Algorithm Based on Clustering for Efficient Image Retrieval

효율적인 영상 검색을 위한 클러스터링 기반 고속 다 해상도 전역 탐색 기법

  • Song, Byeong-Cheol (Dept. of Electronic Computer Science, Korea Advanced Institute of Science and Technology) ;
  • Kim, Myeong-Jun (Dept. of Electronic Computer Science, Korea Advanced Institute of Science and Technology) ;
  • Ra, Jong-Beom (Dept. of Electronic Computer Science, Korea Advanced Institute of Science and Technology)
  • 송병철 (한국과학기술원 전자전산학과) ;
  • 김명준 (한국과학기술원 전자전산학과) ;
  • 라종범 (한국과학기술원 전자전산학과)
  • Published : 2001.03.01
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Abstract

In order to achieve optimal retrieval, i.e., to find the best match to a query according to a certain similarity measure, the exhaustive search should be performed literally for all the images in a database. However, the straightforward exhaustive search algorithm is computationally expensive in large image databases. To reduce its heavy computational cost, this paper presents a fast exhaustive multi-resolution search algorithm based on image database clustering. Firstly, the proposed algorithm partitions the whole image data set into a pre-defined number of clusters having similar feature contents. Next, for a given query, it checks the lower bound of distances in each cluster, eliminating disqualified clusters. Then, it only examines the candidates in the remaining clusters. To alleviate unnecessary feature matching operations in the search procedure, the distance inequality property is employed based on a multi-resolution data structure. The proposed algorithm realizes a fast exhaustive multi-resolution search for either the best match or multiple best matches to the query. Using luminance histograms as a feature, we prove that the proposed algorithm guarantees optimal retrieval with high searching speed.

유사도 측정자 (similarity measure)에 따라 문의자 (query)의 최적 정합자 (the best match)를 찾는 최적 검색 (optimal retrieval)을 위해서는 데이터베이스의 모든 영상들에 대해 전역 탐색 (exhaustive search)을 수행해야 한다. 그러나, 일반적인 전역 탐색은 방대한 계산량을 요구한다. 그 계산량을 줄이기 위해, 본 논문은 영상 데이터베이스의 클러스터링 (clustering)에 기반한 고속 다 해상도 전역 탐색 기법을 제안한다. 먼저 데이터베이스 내의 모든 영상들을 일정 수의 클러스터 (cluster)들로 나눈다. 각 클러스터는 유사한 특징 (feature)을 갖는 영상들로 구성된다. 그리고, 각 클러스터와 문의자 간 거리 (distance)의 하계(lower bound)를 구하고, 가능성이 전혀 없다고 판단될 경우 그 클러스터를 제거한다. 가능성이 있다고 판단된 클러스터들에 속한 후보 영상들 중에서 최적 정합자를 찾는다. 또한, 불필요한 특징 정합 연산을 줄이기 위해 다 해상도 데이터 구조에 기반한 거리 부등식 성질 (distance inequality property)을 유도하여, 탐색 과정에 적용한다. 제안한 기법은 고속 다 해상도 전역 탐색 기법으로서 단일 최적 정합자뿐만 아니라 다수의 상위 최적 정합자들도 정확하게 찾을 수 있다. 가장 보편적인 밝기 히스토그램 (luminance histogram)특징을 사용하여, 제안한 기법이 고속의 탐색 속도와 함께 최적 검색을 보장함을 증명해 보인다.

Keywords

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