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

• Main Authors: Leong, S.H (Author), Ong, S.H (Author)
• Format: Article
• Language: English
• Published: Public Library of Science 2017
• Subjects: algorithm; Algorithms; Bayes theorem; Bayes Theorem; cluster analysis; Cluster Analysis; degradation; human; Humans; image processing; Image Processing, Computer-Assisted; maximum likelihood method; microscopy; Microscopy; Models, Statistical; multimodal imaging; Multimodal Imaging; normal distribution; Normal Distribution; photography; Photography; procedures; simulation; statistical model; statistics and numerical data
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2017 Leong, Ong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.