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.
Highlights
The processing of an image as a whole becomes more challenging with the increase in the image data size
This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation
When sampling is used for scaling down the data size for Gaussian mixture model (GMM) based image processing, the common procedure is to perform unsupervised training through GMM clustering for the pixel sample as described in Section 2.1, and use discriminant analysis to classify the remainder of the image pixels [20,21]
Summary
The processing of an image as a whole becomes more challenging with the increase in the image data size. The proposed sampling based GMM algorithm performs domain adaptation among the clustering results from multiple samples, and improves the existing algorithms, especially [20,21], from three aspects: (i) recovers clusters that have not been identified in the training sample, (ii) recovers small but important clusters, (iii) preserves image features better, and (iv) does not unrealistically pre-define the number of clusters in the whole data set. This paper proposes the second algorithm based on multiple blocks of image data, FlexClustB, to improve the existing algorithms, especially [24,25] from two three aspects: (i) preserves image features better, (ii) reduces computational complexity during clustering of all descriptors by using similarity measure, and (iii) avoids blocking artifact.
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