Download Computational Intelligence for Multimedia Understanding: by Petra Perner (auth.), Emanuele Salerno, A. Enis Çetin, PDF

By Petra Perner (auth.), Emanuele Salerno, A. Enis Çetin, Ovidio Salvetti (eds.)

This booklet constitutes the refereed complaints of the overseas Workshop MUSCLE 2011 on Computational Intelligence for Multimedia knowing, prepared by means of the ERCIM operating crew in Pisa, Italy on December 2011. The 18 revised complete papers have been rigorously reviewed and chosen from over a variety of submissions. The papers disguise the subsequent issues: multisensor platforms, multimodal research, crossmodel facts research and clustering, mixed-reality functions, task and item detection and popularity, textual content and speech reputation, multimedia labelling, semantic annotation, and metadata, multimodal indexing and looking in very huge data-bases; and case studies.

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Additional info for Computational Intelligence for Multimedia Understanding: International Workshop, MUSCLE 2011, Pisa, Italy, December 13-15, 2011, Revised Selected Papers

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4, and computes the mean. 4, this individual named IntensityMean_2011_12_13_1 is shown with its value instantiated with the number obtained). Conclusions and Future Works Extracting relevant features to describe image content is a fundamental step of any application based on the interpretation of such content. Usually, the selection of the features to be extracted is problem-dependent, and this resulted in the proliferation of numbers and numbers of features presented in the literature. In this paper, a systematization of the feature domain is presented.

Once the weights of the seed words are estimated the valence of an unseen word wj can be computed using (1). To select N seeds out of K labeled words, we need to perform feature selection or simply randomly select a subset. Here, we rank all K words by their “worth” as features, using a wrapper feature selector working on a “best-first” forward selection strategy. The performance metric used for feature evaluation is Mean Square Error. Thus, we simply pick the N seeds that are best at predicting the affective rating of the rest of the training data.

Aberer, K. ) Journal on Data Semantics I. LNCS, vol. 2800, pp. 98–127. Springer, Heidelberg (2003) 7. : Ontology-based complex object recognition. Image and Vision Comput. 26(1), 102–113 (2008) 8. : Human-computer interaction for the generation of image processing applications. Int. J. of Human-Computer Studies 69(4), 201–219 (2011) Ontology and Algorithms Integration for Image Analysis 29 9. : Adding Multimedia to the Semantic Web - Building an MPEG-7 Ontology. In: Proc. Int. Semantic Web Working Symposium (SWWS) Stanford, July 30-August 1 (2001) 10.

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