Abstract
Modeling an image or an image-set, which share similar visual contents, by means of a discrete distribution (such as a signature) or by means of a mixture model (such as a Gaussian mixture-model) has a major utility, and may serve as a basis for Content Based Image Retrieval and other related areas. Mixture model can encode information about color, texture, and spatial relationships between colored/textured regions. Image modeling is used in several tasks, such as Image retrieval, Automatic annotation, Unsupervised or Semi-supervised Clustering. Linear optimization techniques offer a reliable and efficient way to compute distance, in both cases, discrete distributions and mixture models. Linear optimization can be also used for modeling image-sets, by computing a mixture model that minimizes distances.
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© 2008 Physica-Verlag Heidelberg
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Julien, C., Saitta, L. (2008). Image and Image-Set Modeling Using a Mixture Model. In: Brito, P. (eds) COMPSTAT 2008. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2084-3_22
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DOI: https://doi.org/10.1007/978-3-7908-2084-3_22
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2083-6
Online ISBN: 978-3-7908-2084-3
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