Multimedia Knowledge and Social Media Analytics Laboratory

Tools

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  • Active Learning

    This toolbox facilitates the application of active learning in multimedia data.

  • AKSDA

    AKSDA is a new, GPU-accelerated, state-of-the-art C++ method (provided both as source code and command-line executable) for supervised dimensionality reduction and classification, using multiple kernels.

  • CERTH @ MediaEval 2011 SED Tool

    This is a Java library implementing the framework presented by CERTH in the Social Event Detection task at MediaEval 2011. It can be used as a competing method for detecting events in large tagged photo collections.

  • CERTH @ MediaEval 2012 SED Tool

    This is a Java library implementing the framework presented by CERTH in the Social Event Detection task at MediaEval 2012. It can be used as a competing method for detecting events in large tagged photo collections.

  • DED - Video Scene Segmentation Evaluation Tool

    This is an implementation of the Differential Edit Distance (DED) metric, a unidimensional measure that has been developed to evaluate video scenes segmentation results.

  • easIE

    An easy-to-use Information Extraction Framework for Corporate Social Responsibility data

  • Environmental Data Storage and Retrieval Service

    The Environmental Data Storage and Retrieval Service is a software, which supports storage, spatiotemporal indexing and retrieval of environmental information as this is provided by nodes that contain environmental measurements

  • f-PocketKRHyper

    f-PocketKRHyper is a fuzzy semantic reasoner, which for the purposes of the LinkedTV EU project is employed to perform content and concept filtering based on semantic descriptions of a user profile and content items

  • GPU-accelerated LIBSVM

    GPU-accelerated LIBSVM is a modification of the original LIBSVM that exploits the CUDA framework to significantly reduce processing time while producing identical results.

  • IAQ

    Image Aesthetic Quality assessment tool – This is a Matlab implementation of the feature extraction process of the Image Aesthetic Quality assessment method presented below.

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