Suchergebnisse

Content-based information retrieval is based on the information contained in documents rather than using metadata such as keywords. Most information retrieval methods are either based on text or image. In this paper, we investigate the usefulness of multimodal features for cross-lingual news search in various domains: politics, health, environment, sport, and finance. To this end, we consider five feature types for image and text and compare the performance of the retrieval system using different combinations. Experimental results show that retrieval results can be improved when considering both visual and textual information. In addition, it is observed that among textual features entity overlap outperforms word embeddings, while geolocation embeddings achieve better performance among visual features in the retrieval task.

Videomining-Algorithmen wie die visuelle Konzeptklassifikation und Personenerkennung sind unerlässlich, um eine feingranulare semantische Suche in großen Videoarchiven wie der historischen Videosammlung der ehemaligen Deutschen Demokratischen Republik (DDR) des Deutschen Rundfunkarchivs (DRA) zu ermöglichen. Wir stellen das Projekt VIVA, unsere Ansätze zur Videoanalyse sowie das VIVA-Softwaretool vor. Letzteres ermöglicht Anwender*innen auf einfache Art, Trainingsdaten zu sammeln, um neue Analysealgorithmen zu trainieren. ; Video mining algorithms such as concept classification and person recognition enable fine-grained semantic search in large video archives like the historical collection of the former German Democratic Republic (GDR) of the German Broadcasting Archive (DRA). We present the project VIVA, our deep learning approaches, and the VIVA software tool, which allows users to easily acquire data to train analysis algorithms. ; Peer Reviewed

Using a Web search engine is one of today's most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.