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 overseas Workshop MUSCLE 2011 on Computational Intelligence for Multimedia realizing, prepared through 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 conceal the next subject matters: multisensor structures, multimodal research, crossmodel facts research and clustering, mixed-reality purposes, job and item detection and popularity, textual content and speech popularity, multimedia labelling, semantic annotation, and metadata, multimodal indexing and looking out in very huge data-bases; and case studies.

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5 are achieved using linear fusion. 6 and our best performance, achieved using linear (or weighted average) fusion, barely matched that at 60%. Finally, non-linear max fusion achieves the best results for 2-way classification at 71%. Subtitles Dataset: Table 3 shows performance in the subtitles dataset. The results are fairly low, with accuracy under 60%, and very small correlation. The relatively poor results show the added complexity of this task compared to unimodal polarity detection in text.

To bootstrap the valence scores for each word in the ChIMP corpus, we used the average sentence-level scores for all sentence where that word appeared. Finally, the ANEW equation system matrix was augmented with all the words in the ChIMP corpus and the valence model in (2) was estimated using LMS. Note that for this training process a 10-fold cross validation experiment was run on the ChIMP corpus sentences. , adding each line twice gives a weight of w = 2. We tested weights of w = 1, w = 2, and using only the samples from ChIMP as training samples (denoted as w = ∞).

Various methods have been proposed to select the initial set of words: seed words may be the lexical labels of affective categories (“anger”,“happiness”), small sets of words with unambiguous (affective) meaning or, even, all words in an affective lexicon. Having a set of seed words and the appropriate similarity measure, the next step is devising a method of combining them to estimate the affective score or category. Once the affective lexicon has been expanded to include all words in our vocabulary, the next step is the combination of word ratings to estimate affective scores for larger lexical units, phrases or sentences.

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