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ABSTRACT
We present data representations, distance measures and organizational structures for fast and efficient retrieval of similar shapes in image databases. Using the Hough Transform we extract shape signatures that correspond to important features of an image. The new shape descriptor is robust against line discontinuities and takes into consideration not only the shape boundaries, but also the content inside the object perimeter. The object signatures are eventually projected into a space that renders them invariant to translation, scaling and rotation. In order to provide support for real-time query-by-content, we also introduce an index structure that hierarchically organizes compressed versions of the extracted object signatures. In this manner we can achieve a significant performance boost for multimedia retrieval. Our experiments suggest that by exploiting the proposed framework, similarity search in a database of 100,000 images would require under 1 sec, using an off-the-shelf personal computer.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 4
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Eamonn Keogh , Li Wei , Xiaopeng Xi , Sang-Hee Lee , Michail Vlachos, LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures, Proceedings of the 32nd international conference on Very large data bases, September 12-15, 2006, Seoul, Korea
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Eamonn Keogh , Li Wei , Xiaopeng Xi , Michail Vlachos , Sang-Hee Lee , Pavlos Protopapas, Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures, The VLDB Journal — The International Journal on Very Large Data Bases, v.18 n.3, p.611-630, June 2009
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