Digital Watermarking of MPEG-4 Facial Animation Parameters by Hartung, Eisert, Girod

By Hartung, Eisert, Girod

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Extra info for Digital Watermarking of MPEG-4 Facial Animation Parameters

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As for that phenomenon, the minimum of two theme models is also taken into consideration by using the following equation, in order to correctly compare the similarity between theme models: |di ∩ dj | |di ∩ dj | +β∗ (2) sim(di , dj ) = α ∗ |di ∪ dj | min(|di |, |dj |) where dj denotes the keyword set of the jth theme, Sim(di , dj ) is the similarity between the ith theme and the jth theme, di ∩ dj is the intersection between di and dj , di ∪ dj is the union of di and dj , |di | is the size of set di , α and β are coefficients, and min(y, z) is the minimum between y and z.

To aggregate the final partitionings into consensus partitioning, a number of well-known methods are employed to make a decisive conclusion. Rest of this paper is organized as follows. In section 2, we explain the proposed method. Section 3 demonstrates results of our proposed method against traditional comparatively. Finally, we conclude in section 4. Fig. 1. Clustering Ensemble Framework 2 Proposed Method In this section, first our proposed clustering ensemble method is briefly outlined, and then its phases are described in detail.

Classifier ensemble as a sub-field of ensemble learning is a general method to improve the performance of classification. At first glance, usage of ensemble learning in clustering sounds similar to the widely prevalent use of combining multiple classifiers to solve difficult classification problems, using techniques such as bagging and boosting. R. Huang et al. ): AMT 2012, LNCS 7669, pp. 32–42, 2012. © Springer-Verlag Berlin Heidelberg 2012 A Clustering Ensemble Based on a Modified Normalized Mutual Information Metric 33 Data clustering or unsupervised learning is an important and very difficult problem.

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