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• W2105264891 abstract "In this paper we propose a new distance metric for probability density functions (PDF). The main advantage of this metric is that unlike the popular Kullback-Liebler (KL) divergence it can be computed in closed form when the PDFs are modeled as Gaussian Mixtures (GM). The application in mind for this metric is histogram based image retrieval. We experimentally show that in an image retrieval scenario the proposed metric provides as good results as the KL divergence at a fraction of the computational cost. This metric is also compared to a Bhattacharyya-based distance metric that can be computed in closed form for GMs and is found to produce better results." @default.
• W2105264891 created "2016-06-24" @default.
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• W2105264891 date "2005-01-01" @default.
• W2105264891 modified "2023-09-29" @default.
• W2105264891 title "An Analytic Distance Metric for Gaussian Mixture Models with Application in Image Retrieval" @default.
• W2105264891 cites W2141782206 @default.
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• W2105264891 doi "https://doi.org/10.1007/11550907_132" @default.
• W2105264891 hasPublicationYear "2005" @default.
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