An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
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This paper introduces a new method of Photomosaic. In this method, we propose to use tiled images that can be rotated in a restricted range. The tiled images are selected from a database. The selection of an image is done by a hashing method based on principal component analysis of a database. After computing the principal components of the database, various kinds of hash tables based on the linear combination of the principal component are prepared beforehand. Using our hashing method, we can reduce the computation time for selecting the tiled images based on the approximated nearest neighbor searching in consideration of a distribution of data in a database. We demonstrate the effectiveness of our hashing method by using a huge number of data in high dimensional space and better looking results of our tiling in experimental results.