Image and Image-Set Modeling Using a Mixture Model

Charbel Julien () and Lorenza Saitta ()
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Charbel Julien: Université Lumière Lyon2, Laboratoire ERIC
Lorenza Saitta: Università del Piemonte Orientale, Dipartimento di Informatica

A chapter in COMPSTAT 2008, 2008, pp 267-275 from Springer

Abstract: 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.

Keywords: image modeling; image-set modeling; discrete distribution; Gaussian mixture model; linear optimization (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1007/978-3-7908-2084-3_22

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