Abstract
Many image modeling and segmentation problems have been tackled using Gaussian Mixture Models (GMM). The two most important issues in image modeling using GMMs is the selection of the appropriate low level features and the specification of the appropriate number of GMM components. In this work we deal with the second issue and present an approach for GMM-based image modeling employing an incremental variational algorithm for Bayesian GMM training that automatically specifies the number of mixture components. Experimental results on natural and texture images indicate that the method yields reasonable models without requiring the a priori specification of the number of components.
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Constantinopoulos, C., Likas, A. (2007). Image Modeling and Segmentation Using Incremental Bayesian Mixture Models. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_74
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DOI: https://doi.org/10.1007/978-3-540-74272-2_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74271-5
Online ISBN: 978-3-540-74272-2
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