By Hartung, Eisert, Girod
Read Online or Download Digital Watermarking of MPEG-4 Facial Animation Parameters PDF
Best media books
American journalism is collapsing as newspapers and magazines fail and rankings of journalists are laid off around the nation. traditional knowledge says the net is in charge, yet veteran reporters and media critics Robert W. McChesney and John Nichols disagree. The drawback of yankee journalism predates the good Recession and electronic media increase.
Pop culture---and the ads that surrounds it---teaches younger boys and girls 5 myths approximately intercourse and sexuality:
-Girls don't decide on boys, boys opt for girls--but in basic terms attractive ladies
-There's just one type of sexy--slender, curvy, white attractiveness
-Girls may still paintings to be that kind of horny
-The more youthful a lady is, the sexier she is
-Sexual violence should be sizzling
Together, those 5 myths make up the Lolita impact, the mass media developments that paintings to undermine girls' self-confidence, that condone girl objectification, and that tacitly foster intercourse crimes. yet settling on those myths and breaking them down may help women learn how to realize revolutionary and fit sexuality and defend themselves from degrading media rules and sexual vulnerability. within the Lolita influence, Dr. M. Gigi Durham bargains step forward recommendations for empowering women to make fit judgements approximately their very own sexuality.
Elementare ? konomische Konzepte werden vorgestellt und auf die Medienbranche angewendet. Printmedien, Radio, Fernsehen und Multimediaanwendungen werden auf ihre ? konomischen Gesetzm? ?igkeiten und ihre Beziehungen untereinander hin analysiert; die examine wird in den Kontext aktueller Geschehnisse in der Medienbranche eingebettet.
This ebook constitutes the refereed lawsuits of the seventh overseas convention on lively Media know-how, AMT 2011, held in Lanzhou, China, in September 2011. The 30 revised complete papers and six keynote talks have been rigorously reviewed and chosen for inclusion within the e-book. they're grouped in topcial sections on facts mining and trend research in energetic media; energetic human-Web interplay and social media; energetic net intelligence purposes; lively multi-agent and community structures; in addition to expertise intelligence.
- Murdoch's World: The Last of the Old Media Empires
- Avid Media Composer 6.x Cookbook
- Emotional Governance: New Ideas on Media and Democratic Leadership
- Understanding Representation (Understanding Contemporary Culture series)
- Life: The Movie: How Entertainment Conquered Reality
Extra info for Digital Watermarking of MPEG-4 Facial Animation Parameters
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.