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

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

This ebook constitutes the refereed lawsuits of the foreign Workshop MUSCLE 2011 on Computational Intelligence for Multimedia figuring out, equipped by means of the ERCIM operating staff 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 themes: multisensor platforms, multimodal research, crossmodel information research and clustering, mixed-reality purposes, task and item detection and popularity, textual content and speech attractiveness, multimedia labelling, semantic annotation, and metadata, multimodal indexing and looking in very huge data-bases; and case studies.

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

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A simple scenario can be reported to illustrate the system currently developed for testing IFO. A series of steps can be identified as shown in Fig. 4.  1 2 3 Initially, at the step 1, the user is asked to select an image. Once an image has been selected, the system automatically creates a corresponding individual of the class Image into IFO. gov/ij/ Ontology and Algorithms Integration for Image Analysis 27 Fig. 4. The sequence of steps for the sample scenario. (A) the selected image; (B) image characteristics used to instantiate the image individual’s properties; (C) the query launched to select the features computable according to image colour model; (D) an excerpt of the list of features resulting from the query; among them IntensityMean is selected; (E) the query launched to check the operation required by IntensityMean; (F) the results of the histogram computation by ImageJ; (G) the feature individual instantiated with the computed value.

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. : COMM: Designing a WellFounded Multimedia Ontology for the Web. , Cudré-Mauroux, P. ) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 30–43. Springer, Heidelberg (2007) 11. : Evaluating the Application of Semantic Inferencing Rules to Image Annotation.

1 Word Level Tagging - Metrics of Semantic Similarity We aim at characterizing the affective content of words in a continuous valence range of [−1, 1] (from very negative to very positive), from the reader perspective. wN are the seed words, v(wi ) is the valence rating for seed word wi , ai is the weight corresponding to word wi (that is estimated as described next), and d(wi , wj ) is a measure of semantic similarity between words wi and wj . Assume a training corpus of K words with known ratings and a set of N < K seed words for which we need to estimate weights ai , we can use (1) to create a system of K linear equations with N + 1 unknown variables as shown next: EmotiWord: Affective Lexicon Creation ⎡ 1 d(w1 , w1 )v(w1 ) · · · ⎢ ..

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