BUDÍKOVÁ, Petra, Michal BATKO and Pavel ZEZULA. Multi-modal Image Retrieval for Search-based Image Annotation with RF. Online. In 2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018). NEW YORK: IEEE, 2018, p. 52-60. ISBN 978-1-5386-6857-3. Available from: https://dx.doi.org/10.1109/ISM.2018.00017.
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Basic information
Original name Multi-modal Image Retrieval for Search-based Image Annotation with RF
Authors BUDÍKOVÁ, Petra (203 Czech Republic, guarantor, belonging to the institution), Michal BATKO (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution).
Edition NEW YORK, 2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018), p. 52-60, 9 pp. 2018.
Publisher IEEE
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/18:00101829
Organization unit Faculty of Informatics
ISBN 978-1-5386-6857-3
Doi http://dx.doi.org/10.1109/ISM.2018.00017
UT WoS 000459863600009
Keywords in English image annotation; relevance feedback; multi-modal image retrieval
Tags DISA, firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 30/4/2019 06:57.
Abstract
Search-based annotation methods can be used for proposing descriptive keywords to users who need to annotate images e.g. in image stock databases. From the annotation output, users select keywords which they want to assign to the given image. The selected keywords can serve as a relevance feedback for additional annotation refinement. In this paper, we study the possibilities of exploiting the annotation relevance feedback, which is a novel problem that has not been systematically addressed yet. In particular, we focus on the subtask of utilizing the feedback for the retrieval of related annotated images that are subsequently used for mining of candidate keywords. We select three multi-modal search techniques that can be applied to this problem, implement them within a state-of-the-art search-based annotation system, and experimentally evaluate their usefulness for annotation quality improvement.
Links
GA16-18889S, research and development projectName: Analytika pro velká nestrukturovaná data (Acronym: Big Data Analytics for Unstructured Data)
Investor: Czech Science Foundation
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