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Ktisis
Article . 2020
Data sources: Ktisis
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ZENODO; International Journal of Multimedia Information Retrieval
Article . 2020 . Peer-reviewed
License: CC BY NC ND
Data sources: ZENODO; Crossref
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Image annotation: the effects of content, lexicon and annotation method

Authors: Zenonas Theodosiou; Nicolas Tsapatsoulis;

Image annotation: the effects of content, lexicon and annotation method

Abstract

Image annotation is the process of assigning metadata to images, allowing effective retrieval by text-based search techniques. Despite the lots of efforts in automatic multimedia analysis, automatic semantic annotation of multimedia is still inefficient due to the problems in modeling high-level semantic terms. In this paper, we examine the factors affecting the quality of annotations collected through crowdsourcing platforms. An image dataset was manually annotated utilizing: (1) a vocabulary consists of preselected set of keywords, (2) an hierarchical vocabulary and (3) free keywords. The results show that the annotation quality is affected by the image content itself and the used lexicon. As we expected while annotation using the hierarchical vocabulary is more representative, the use of free keywords leads to increased invalid annotation. Finally, it is shown that images requiring annotations that are not directly related to their content (i.e., annotation using abstract concepts) lead to accrue annotator inconsistency revealing in that way the difficulty in annotating such kind of images is not limited to automatic annotation, but it is a generic problem of annotation.

Country
Cyprus
Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Vocabulary Process (engineering) Computer science media_common.quotation_subject Crowdsourcing computer.software_genre Lexicon Set (abstract data type) Annotation media_common business.industry Metadata Automatic image annotation Artificial intelligence business computer Natural language processing

Keywords

Computer and Information Sciences, Library and Information Sciences, Annotation quality, Media Technology, Image annotation, Manual annotation, Crowdsourcing, Natural Sciences, Information Systems

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  • citations
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    7
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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visibility
download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
7
Top 10%
Average
Top 10%
68
106
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