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        <title>Multilingual Image Corpus: Annotation Protocol</title>
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        <namePart type="given">Svetla</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>In this paper, we present work in progress aimed at the development of a new image dataset with annotated objects. The Multilingual Image Corpus consists of an ontology of visual objects (based on WordNet) and a collection of thematically related images annotated with segmentation masks and object classes. We identified 277 dominant classes and 1,037 parent and attribute classes, and grouped them into 10 thematic domains such as sport, medicine, education, food, security, etc. For the selected classes a large-scale web image search is being conducted in order to compile a substantial collection of high-quality copyright free images. The focus of the paper is the annotation protocol which we established to facilitate the annotation process: the Ontology of visual objects and the conventions for image selection and for object segmentation. The dataset is designed both for image classification and object detection and for semantic segmentation. In addition, the object annotations will be supplied with multilingual descriptions by using freely available wordnets.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.80</url>
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%0 Conference Proceedings
%T Multilingual Image Corpus: Annotation Protocol
%A Koeva, Svetla
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F koeva-2021-multilingual
%X In this paper, we present work in progress aimed at the development of a new image dataset with annotated objects. The Multilingual Image Corpus consists of an ontology of visual objects (based on WordNet) and a collection of thematically related images annotated with segmentation masks and object classes. We identified 277 dominant classes and 1,037 parent and attribute classes, and grouped them into 10 thematic domains such as sport, medicine, education, food, security, etc. For the selected classes a large-scale web image search is being conducted in order to compile a substantial collection of high-quality copyright free images. The focus of the paper is the annotation protocol which we established to facilitate the annotation process: the Ontology of visual objects and the conventions for image selection and for object segmentation. The dataset is designed both for image classification and object detection and for semantic segmentation. In addition, the object annotations will be supplied with multilingual descriptions by using freely available wordnets.
%U https://aclanthology.org/2021.ranlp-1.80
%P 701-707

Markdown (Informal)

[Multilingual Image Corpus: Annotation Protocol](https://aclanthology.org/2021.ranlp-1.80) (Koeva, RANLP 2021)

• Multilingual Image Corpus: Annotation Protocol (Koeva, RANLP 2021)

ACL

• Svetla Koeva. 2021. Multilingual Image Corpus: Annotation Protocol. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 701–707, Held Online. INCOMA Ltd..