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The earth mover's distance (EMD) is a measure of the distance between two distributions, and it has been widely used in multimedia information retrieval systems, in particular, in content-based image retrieval systems. When the EMD is applied to image problems based on color or texture, the EMD reflects the human perceptual similarities. However, its computations are too expensive to use in large-scale databases. In order to achieve efficient computation of the EMD during query processing, we have developed “fastEMD,” a library for high-speed feature-based similarity retrievals in large databases. This paper introduces techniques that are used in the implementation of the fastEMD and performs extensive experiments to demonstrate its efficiency.

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Automatic meta-data annotation of images region is essentially important for Cross-Media Information Retrieval between texts and images. In this paper, we propose an automatic meta-data annotation of images region. We apply and discuss Gaussian mixture models for this problem. The annotation meta-data of each region is prepared from top 5 of the log likelihood. This method can annotate a number of language meta-data because it annotates the language meta-data in each region. The experimental results show that the accuracy of automatic annotation meta-data to top 5 achieved about 70%.

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