Abstract
As for unsupervised image segmentation, one important application is content based image retrieval. In this context, the key problem is to automatically determine the number of regions(i.e., clusters) for each image so that we can then perform a query on the region of interest. This paper presents an iterative entropy regularized likelihood (ERL) learning algorithm for cluster analysis based on a mixture model to solve this problem. Several experiments have demonstrated that the iterative ERL learning algorithm can automatically detect the number of regions in a image and outperforms the generalized competitive clustering.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lu, Z. (2006). Unsupervised Image Segmentation Using an Iterative Entropy Regularized Likelihood Learning Algorithm. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_72
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DOI: https://doi.org/10.1007/11760023_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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