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2015 IEEE International Symposium on Multimedia (ISM) (2015)
Miami, FL, USA
Dec. 14, 2015 to Dec. 16, 2015
ISBN: 978-1-5090-0378-5
pp: 237-240
Near-duplicate image detection plays an important role in several real applications. Such task is usually achieved by applying a clustering algorithm followed by refinement steps, which is a computationally expensive process. In this paper we introduce a framework based on a novel similarity join operator, which is able both to replace and speed up the clustering step, whereas also releasing the need of further refinement processes. It is based on absolute and relative similarity ratios, ensuring that top ranked image pairs are in the final result. Experiments performed on real datasets shows that our proposal is up to three orders of magnitude faster than the best techniques in the literature, always returning a high-quality result set.
Feature extraction, Proposals, Multimedia communication, Measurement, Visualization, Computers, Clustering algorithms

L. O. Carvalho, L. F. Santos, W. D. Oliveira, A. J. Traina and C. Traina, "Self Similarity Wide-Joins for Near-Duplicate Image Detection," 2015 IEEE International Symposium on Multimedia (ISM), Miami, FL, USA, 2016, pp. 237-240.
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