29,877 research outputs found
Learning from Ontology Streams with Semantic Concept Drift
Data stream learning has been largely studied for extracting knowledge
structures from continuous and rapid data records. In the semantic Web, data is
interpreted in ontologies and its ordered sequence is represented as an
ontology stream. Our work exploits the semantics of such streams to tackle the
problem of concept drift i.e., unexpected changes in data distribution, causing
most of models to be less accurate as time passes. To this end we revisited (i)
semantic inference in the context of supervised stream learning, and (ii)
models with semantic embeddings. The experiments show accurate prediction with
data from Dublin and Beijing
Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases
In this paper, we introduce a novel framework for automatic Semantic Video Annotation. As this framework detects possible events occurring in video clips, it forms the annotating base of video search engine. To achieve this purpose, the system has to able to operate on uncontrolled wide-domain videos. Thus, all layers have to be based on generic features.
This framework aims to bridge the "semantic gap", which is the difference between the low-level visual features and the human's perception, by finding videos with similar visual events, then analyzing their free text annotation to find a common area then to decide the best description for this new video using commonsense knowledgebases.
Experiments were performed on wide-domain video clips from the TRECVID 2005 BBC rush standard database. Results from these experiments show promising integrity between those two layers in order to find expressing annotations for the input video. These results were evaluated based on retrieval performance
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