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in a keyword-based photo retrieval process.We use metadata about the photo shot context (address location, nearby objects, season, light status...) to generate a bag of words for indexing each photo. We extend the Vector Space Model in order to transform these shot context words into document-vector terms. In addition
. Current techniques for IR including keyword based, content based and ontology based image retrieval have several unsolved issues. We promote the ontology based IR approach and focus on two issues: firstly, the difficulty in constructing ontologies of images for those industries without ontology professionals, and, secondly
include visual concepts together with linguistic keywords, are therefore needed to support video annotation up to the level of detail of pattern specification. This paper presents pictorially enriched ontologies and provides a solution for their implementation in the soccer video domain. The pictorially enriched ontology is
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into
representation model, semantic information building and semantic retrieval techniques. In this paper, we proposed a novel framework for image semantic annotation and retrieval. It employs a semantic network as the semantic representation model, and uses the combination of semantic keywords, linguistic ontology and low-level
The construction of Automated Digital Libraries has grabbed widespread popularity over recent years, rendering the value added services that suit to the tailored user requirements. In this context, carrying out traditional searches over text material by querying upon a given metadata or a set of domain keywords is
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