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The Implementation of Cluster Identification of Data

Muhamad Zulkapley, Zafrrah (2007) The Implementation of Cluster Identification of Data. Universiti teknologi petronas. (Unpublished)

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In this paper, we combine few transformations to create a single unique transformation based on Threshold, Edge Detector, Simple Skeleton and Hough Line Transformation with information-theoretic-based criteria for unsupervised hierarchical image-set clustering. The continuous image modeling is based on mixture of Gaussian densities. The unsupervised image-set clustering is based on a generalized version of a recently introduced information-theoretic principle, the information bottleneck principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. Experimental results demonstrate the pattern of the image skeleton. Information theoretic tools are used to evaluate cluster quality. Particular emphasis is placed on the application of the clustering for efficient image search and verification. The application is very suit to offer authentic-looking counterfeit checks.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Users 2053 not found.
Date Deposited: 24 Oct 2013 09:46
Last Modified: 25 Jan 2017 09:45
URI: http://utpedia.utp.edu.my/id/eprint/9558

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