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Drakopoulos Georgios Kafeza Eleanna Mylonas Phivos Iliadis Lazaros 《Neural computing & applications》2021,33(23):16363-16375
Neural Computing and Applications - Graph signal processing has recently emerged as a field with applications across a broad spectrum of fields including brain connectivity networks, logistics and... 相似文献
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Phivos Mylonas Thanos Athanasiadis Manolis Wallace Yannis Avrithis Stefanos Kollias 《Multimedia Tools and Applications》2008,39(3):293-327
In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories.
Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging
of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input
visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying
semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation
model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the
index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic
processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. The first part of this
work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic
indexing, while the second part will continue on the use of the extracted semantic information for personalized retrieval.
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Stefanos KolliasEmail: |
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Yannis Kalantidis Giorgos Tolias Yannis Avrithis Marios Phinikettos Evaggelos Spyrou Phivos Mylonas Stefanos Kollias 《Multimedia Tools and Applications》2011,51(2):555-592
New applications are emerging every day exploiting the huge data volume in community photo collections. Most focus on popular
subsets, e.g., images containing landmarks or associated to Wikipedia articles. In this work we are concerned with the problem
of accurately finding the location where a photo is taken without needing any metadata, that is, solely by its visual content.
We also recognize landmarks where applicable, automatically linking them to Wikipedia. We show that the time is right for
automating the geo-tagging process, and we show how this can work at large scale. In doing so, we do exploit redundancy of
content in popular locations—but unlike most existing solutions, we do not restrict to landmarks. In other words, we can compactly
represent the visual content of all thousands of images depicting e.g., the Parthenon and still retrieve any single, isolated,
non-landmark image like a house or a graffiti on a wall. Starting from an existing, geo-tagged dataset, we cluster images
into sets of different views of the same scene. This is a very efficient, scalable, and fully automated mining process. We
then align all views in a set to one reference image and construct a 2D scene map. Our indexing scheme operates directly on scene maps. We evaluate our solution on a challenging one million urban image dataset
and provide public access to our service through our online application, VIRaL. 相似文献
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Taking advantage of the continuously improving, web-based learning systems plays an important role for self-learning, especially in the case of working people. Nevertheless, learning systems do not generally adapt to learners’ profiles. Learners have to spend a lot of time before reaching the learning goal that is compatible with their knowledge background. To overcome such difficulties, an e-learning schema is introduced that adapts to the learners’ ICT (Information and Communication Technologies) knowledge level. The IEEE Reference Model (WG 1) defined by the Learning Technology Standards Committee (LTSA) is extended and used for this purpose. The proposed approach is based on the usage of electronic questionnaires (e-questionnaires) designed by a group of experts. Through the automatic analysis of the learners’ responses to the questionnaires, all learners are assigned to different learner profiles. According to these profiles they are served with learning material that best matches their educational needs. We have implemented our approach in five European countries and the overall case study illustrates very promising results. 相似文献
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