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TU Berlin

Inhalt des Dokuments

Online Demonstratoren

Dies sind einige Interaktive Tools zur Visualisierung der Forschungsergebnisse

Geotagging in Social Networks


We present a hierarchical, multi-modal approach for placing Flickr videos on the map. Our approach makes use of external resources to identify toponyms in the metadata and of visual and textual features to identify similar content. First, the geographical boundaries extraction method identi es the country and its dimension. We use a database of more than 3.6 million Flickr images to group them together into geographical regions and to build a hierarchical model. A fusion of visual and textual methods is used to classify the videos location into possible regions. Next, the visually nearest neighbour method uses a nearest neighbour approach to nd correspondences with the training images within the preclassified regions. The video sequences are represented using low-level feature vectors from multiple key frames. The Flickr videos are tagged with the geo-information of the visually most similar training item within the regions that is previously ltered by the pre-classi cation step for each test video. The results show that we are able to tag one third of our videos correctly within an error of 1 km. Demo

Genre Detection

TU Berlin
TU Berlin

This demonstrator describes the possibilities of cross-modal classification of multimedia documents in social media platforms. Our framework predicts the user-chosen category of consumer-produced video sequences based on their textual and visual features.


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