Approximate Gaussian Mixtures for Large Scale Vocabularies

Yannis Avrithis and Yannis Kalantidis

Abstract. We introduce a clustering method that combines the flexibility of Gaussian mixtures with the scaling properties needed to construct visual vocabularies for image retrieval. It is a variant of expectation-maximization that can converge rapidly while dynamically estimating the number of components. We employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed and precision. We achieve superior performance in large scale retrieval, being as fast as the best known approximate k-means.

Keywords: Gaussian mixtures, expectation-maximization, visual vocabularies, large scale clustering, approximate nearest neighbor search

LNCS 7574, p. 15 ff.

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