Abstract
Among the various approaches of content-based image modeling, generative models have become prominent due to their ability of approximating feature distributions with arbitrary accuracy. A frequently encountered generative model for the purpose of content-based image retrieval is the Gaussian mixture model which facilitates the application of various dissimilarity measures. The question of which dissimilarity measure provides the highest retrieval performance in terms of accuracy and efficiency is still an open research question. In this paper, we propose an empirical investigation of dissimilarity measures for Gaussian mixture models based on high-dimensional local feature descriptors. To this end, we include a unifying overview of state-of-the-art dissimilarity measures applicable to Gaussian mixture models along with an extensive performance analysis on a multitude of local feature descriptors. Our findings will help to guide further research in the field of content-based image modeling with Gaussian mixture models.
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Beecks, C., Uysal, M.S., Seidl, T. (2015). Content-Based Image Retrieval with Gaussian Mixture Models. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_26
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DOI: https://doi.org/10.1007/978-3-319-14445-0_26
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