Enrichment and ranking of the YouTube tag space and integration with the Linked Data cloud

The increase of personal digital cameras with video functionality and video-enabled camera phones has increased the amount of user-generated videos on the Web. People are spending more and more time viewing online videos as a major source of entertainment and “infotainment”. Social websites allow users to assign shared free-form tags to user-generated multimedia resources, thus generating annotations for objects with a minimum amount of effort. Tagging allows communities to organise their multimedia items into browseable sets, but these tags may be poorly chosen and related tags may be omitted. Current techniques to retrieve, integrate and present this media to users are deficient and could do with improvement. In this paper, we describe a framework for semantic enrichment, ranking and integration of web video tags using Semantic Web technologies. Semantic enrichment of folksonomies can bridge the gap between the uncontrolled and flat structures typically found in user-generated content and structures provided by the Semantic Web. The enhancement of tag spaces with semantics has been accomplished through two major tasks: a tag space expansion and ranking step; and through concept matching and integration with the Linked Data cloud. We have explored social, temporal and spatial contexts to enrich and extend the existing tag space. The resulting semantic tag space is modelled via a local graph based on co-occurrence distances for ranking. A ranked tag list is mapped and integrated with the Linked Data cloud through the DBpedia resource repository. Multi-dimensional context filtering for tag expansion means that tag ranking is much easier and it provides less ambiguous tag to concept matching

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale

Incremental Multiple Classifier Active Learning for Concept Indexing in Images and Videos

Regular Papers: Multimedia Indexing and MiningInternational audienceActive learning with multiple classifiers has shown good performance for concept indexing in images or video shots in the case of highly imbalanced data. It involves however a large number of computations. In this paper, we propose a new incremental active learning algorithm based on multiple SVM for image and video annotation. The experimental result show that the best performance (MAP) is reached when 15-30% of the corpus is annotated and the new method can achieve almost the same precision while saving 50 to 63% of the computation time

An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding

Abstract. This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and pattern recognition, relies also on general knowledge and application context for understanding visual content in conceptual terms. Our work is an attempt to imitate this behavior by devising an evidence driven prob-abilistic inference framework using ontologies and bayesian networks. Experiments conducted for two different image analysis tasks showed improvement in performance, compared to the case where computer vision techniques act isolated from any type of knowledge or context.