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 e-book constitutes the refereed complaints of the overseas Workshop MUSCLE 2011 on Computational Intelligence for Multimedia realizing, equipped through the ERCIM operating workforce in Pisa, Italy on December 2011. The 18 revised complete papers have been rigorously reviewed and chosen from over a number of submissions. The papers disguise the subsequent issues: multisensor platforms, multimodal research, crossmodel facts research and clustering, mixed-reality purposes, job 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 resources for Computational Intelligence for Multimedia Understanding: International Workshop, MUSCLE 2011, Pisa, Italy, December 13-15, 2011, Revised Selected Papers

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Rapid object detection using boosted cascade of simple features. In: IEEE CVPR, pp. 511–518. IEEE Press, New York (2001) 4. : Comparison of local feature descriptors for mobile visual search. In: ICIP, pp. 3885–3888 (2010) 5. : Comparative Analysis and Classification of Features for Image Models. Pattern Rec. and Image Analysis 16(3), 265–297 (2006) 6. : IF-Map: An Ontology-Mapping Method Based on Information-Flow Theory. , Aberer, K. ) Journal on Data Semantics I. LNCS, vol. 2800, pp. 98–127.

Results are shown for the three fusion methods (average, weighted average, maximum). For the ChIMP politeness detection task, performance of the baseline (unsupervised) model is lower than that quoted in [22] for lexical features. Performance improves significantly by adapting the affective model using in-domain ChIMP data reaching up to 84% accuracy for linear fusion (matching the results in [22]). The best results for frustration detection are achieved with the baseline model and max fusion schemes at 66% (at least as good as those reported in [22]).

So far, several ontologies have been defined for the image processing domain. Some of them contain a conceptualization of image features, but aim at specific image processing tasks. A Visual Concept Ontology has been proposed in [7] and recently re-used in [8] to define a general framework able to support the automatic generation of image processing applications (ranging from image pre-processing tasks, such as enhancement and restoration, to image segmentation tasks). Other ontologies have been developed to support semantic annotations of images [9, 10].

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