The reuse of scientific raw data is a key demand of Open Science. In the project NOA we foster reuse of scientific images by collecting and uploading them to Wikimedia Commons. In this paper we present a text-based annotation method that proposes Wikipedia categories for open access images. The assigned categories can be used for image retrieval or to upload images to Wikimedia Commons. The annotation basically consists of two phases: extracting salient keywords and mapping these keywords to categories. The results are evaluated on a small record of open access images that were manually annotated
The development of technology generates huge amounts of non-textual information, such as images. An ...
In this paper, we introduce an expansion and reranking approach for annotation based image retrieval...
In this survey, we argue that using structured vocabularies is capital to the success of image annot...
In der vorliegenden Masterarbeit geht es um die automatische Annotation von Bildern mithilfe der Kat...
NOA is a search engine for scientific images from open access publications based on full text indexi...
Multimedia objects, especially images and figures, are essential for the visualization and interpret...
Abstract. In this work, we compare two simple methods of tagging scientific publications with labels...
Data annotations are an important kind of metadata that occur in the form of externally assigned des...
Here we present how two independent infrastructures, Wikimedia and iNaturalist, can be jointly lever...
Summarization: In this paper a novel approach for automatically annotating image databases is propos...
The information environment of the first decade of the XXIst century is unprecedented. The physical ...
NOA is a project that extracts figures from scientific open access articles, stores them and makes t...
This paper describes SW1, the first version of a semantically annotated snapshot of the EnglishWikip...
13 pagesInternational audienceIn this survey, we argue that using structured vocabularies is capital...
This study illustrates the challenges of developing a broad Wikipedia thematic landscape. Particular...
The development of technology generates huge amounts of non-textual information, such as images. An ...
In this paper, we introduce an expansion and reranking approach for annotation based image retrieval...
In this survey, we argue that using structured vocabularies is capital to the success of image annot...
In der vorliegenden Masterarbeit geht es um die automatische Annotation von Bildern mithilfe der Kat...
NOA is a search engine for scientific images from open access publications based on full text indexi...
Multimedia objects, especially images and figures, are essential for the visualization and interpret...
Abstract. In this work, we compare two simple methods of tagging scientific publications with labels...
Data annotations are an important kind of metadata that occur in the form of externally assigned des...
Here we present how two independent infrastructures, Wikimedia and iNaturalist, can be jointly lever...
Summarization: In this paper a novel approach for automatically annotating image databases is propos...
The information environment of the first decade of the XXIst century is unprecedented. The physical ...
NOA is a project that extracts figures from scientific open access articles, stores them and makes t...
This paper describes SW1, the first version of a semantically annotated snapshot of the EnglishWikip...
13 pagesInternational audienceIn this survey, we argue that using structured vocabularies is capital...
This study illustrates the challenges of developing a broad Wikipedia thematic landscape. Particular...
The development of technology generates huge amounts of non-textual information, such as images. An ...
In this paper, we introduce an expansion and reranking approach for annotation based image retrieval...
In this survey, we argue that using structured vocabularies is capital to the success of image annot...