Titlebook: Recognizing Patterns in Signals, Speech, Images, and Videos; ICPR 2010 Contents, Devrim Ünay,Zehra Çataltepe,Selim Aksoy Conference procee

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Verification of Video Source Camera Competition (,) to create a benchmark for source video identification, where the videos originate from YouTube. Despite the number of participants was satisfactory initially, only two participants submitted results, mostly due to a lack of time. Judging by the performance of the contestants, this is certainly not a trivial problem.

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Feature-Based Dissimilarity Space Classificationconcluded that the feature-based dissimilarity space classification performs similar or better than the linear and nonlinear SVMs, as averaged over all 301 datasets of the contest and in a large subset of its datasets. This indicates that these classifiers have their own domain of competence.

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A Fuzzy-Interval Based Approach for Explicit Graph Embeddingare obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR.

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The University of Surrey Visual Concept Detection System at ImageCLEF@ICPR: Working Notes learning. The main novelty is the classifier-level and kernel-level fusion using Kernel Discriminant Analysis with RBF/Power Chi-Squared kernels obtained from various image descriptors. For 32 out of 53 individual concepts, we obtain the best performance of all 12 submissions to this task.

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Verification of Video Source Camera Competition (,)emselves may be illegal. Due to the tight integration of these mobile devices with the internet, these recordings may quickly find their way to internet video-sharing sites such as YouTube. In criminal casework it is advantageous to reliably establish the source of the video. Although this was shown