Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing

Leong, S.H. and Ong, S.H. (2017) Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing. PLoS ONE, 12 (7). e0180307. ISSN 1932-6203

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Official URL: https://doi.org/10.1371/journal.pone.0180307

Abstract

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.

Item Type: Article
Uncontrolled Keywords: Algorithms; Bayes Theorem; Cluster Analysis; Humans; Image Processing, Computer-Assisted; Microscopy; Models, Statistical; Multimodal Imaging; Normal Distribution; Photography
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Faculty of Science > Institute of Mathematical Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 05 Sep 2018 03:55
Last Modified: 05 Sep 2018 03:55
URI: http://eprints.um.edu.my/id/eprint/19113

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