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Proceedings Paper

Fast image retrieval based on K-means clustering and multiresolution data structure for large image databases
Author(s): Byung Cheol Song; Myung Jun Kim; Jong Beom Ra
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Paper Abstract

This paper presents a fast search algorithm based on multi- resolution data structure for efficient image retrieval in large image databases. The proposed algorithm consists of two stages: a database-building stage and a searching stage. In the database-building stage, we partition the image data set into a pre-defined number of clusters by using the MacQueen K-means clustering algorithm. The searching stage has the two steps to choose proper clusters and to find the best match among all the images included in the chosen clusters. In order to reduce the heavy computational cost in the searching stage, we proposed two kinds of fast exhaustive searching algorithms based on the multi- resolution feature space, which guarantee a perfect retrieval accuracy of 100%. By applying these two algorithms to the searching stage, we can find the best match with very high search speed and accuracy. In addition, we consider a retrieval scheme producing multiple output images including the best match. Intensive simulation results show that the proposed schemes provide a prospective search performance.

Paper Details

Date Published: 30 May 2000
PDF: 12 pages
Proc. SPIE 4067, Visual Communications and Image Processing 2000, (30 May 2000); doi: 10.1117/12.386575
Show Author Affiliations
Byung Cheol Song, Korea Advanced Institute of Science and Technology (South Korea)
Myung Jun Kim, Korea Advanced Institute of Science and Technology (South Korea)
Jong Beom Ra, Korea Advanced Institute of Science and Technology (South Korea)


Published in SPIE Proceedings Vol. 4067:
Visual Communications and Image Processing 2000
King N. Ngan; Thomas Sikora; Ming-Ting Sun, Editor(s)

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