Publikation: Learning recognition of semantically re...
Stammdaten
Titel: | Learning recognition of semantically relevant video segments from endoscopy videos contributed and edited in a private social network |
Untertitel: | |
Kurzfassung: | Besides the great benefit of minimizing intrusions made in body,
endoscopic surgery has the advantage of producing abundant documentation
regarding the procedure as well. Recordings can be used not only to
document the surgery but as a mean for learning and improving experts
knowledge too. To minimize time and effort that experts invest in
preparing informative endoscopic videos, tools that can automatically
identify interesting parts in videos are needed. To achieve this, an
annotated data set is required. This paper presents an approach for
collecting endoscopic videos and related experts knowledge. For this, a
social network with integrated video annotation and presentation tools
is used. Experts can upload, annotate and share their videos with other
physicians. In the background their interactions with the videos are
recorded, interpreted and used to derive predictive models or improve
existing ones. Once a prediction model is derived, its results will be
displayed to physicians as suggestions, which can be integrated into
their video annotations. Physicians choice to either keep these
suggestions or discard them will serve as a feedback to the learned
model and used to refine the derived knowledge. |
Schlagworte: |
Publikationstyp: | Beitrag in Sammelwerk (Autorenschaft) |
Erscheinungsdatum: | 01.01.2014 (Print) |
Erschienen in: |
MM '14 Proceedings of the ACM International Conference on Multimedia
MM '14 Proceedings of the ACM International Conference on Multimedia
(
ACM - New York;
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 663 - 666 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1145/2647868.2654860 |
Homepage: | - |
Open Access |
|
Erscheinungsdatum: | 01.01.2014 |
ISBN: |
|
ISSN: | - |
Homepage: | http://dl.acm.org/citation.cfm?id=2654860&CFID=603292169&CFTOKEN=50052745 |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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