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• Roland Kwitt (Maintainer)

This code implements the Embedded Multiresolution Mixture Models (EMMs), as proposed by Vasconcelos, N. and Lippman, A. We provide MATLAB files for fitting EMMs and similarity measurement by using the Feature Likelihood as well as two approximations to the Kullback-Leibler divergence between multivariate Gaussian mixture models. The tarball includes a PDF file documenting the implementation of the algorithms and further lists additional references.

• Vasconcelos, N. and Lippman, A., A Probabilistic Architecture for Content-Based Image Retrieval, In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'00), pp. 216--221, 2000, Hilton Head, South Carolina, USA
• Vasconcelos, N., On the Efficient Evaluation of Probabilistic Similarity Functions for Image Retrieval, In: IEEE Transactions on Information Theory, vol. 50, issue 7, pp. 1482-1496, Jul. 2004
• Goldberger, J. et al., An Efficient Image Similarity Measure Based on Approximations of the KL-divergence between two Gaussian Mixtures, In: Proceedings of the IEEE International Conference on Computer Vision (ICCV'03), pp. 487-493, 2003, Nice, France

Current download: emm-2472009.tgz

For older versions of the source code, see here