<div class="csl-bib-body">
<div class="csl-entry">Bilgili, A., & Sen, A. (2023). A Framework for Automatic Selection of Indoor Landmarks using Machine Learning Algorithms and Shapley Additive Explanations. In <i>Proceedings of the 18th International Conference on Location Based Services</i> (pp. 25–30). https://doi.org/10.34726/5702</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/194709
-
dc.identifier.uri
https://doi.org/10.34726/5702
-
dc.description.abstract
Landmarks are salient objects in an environment compared to their surroundings. However, a challenge of landmark-based navigation is selecting the most salient landmarks to include in route instructions. Current approaches mainly adopt weighted linear models, which assume that landmarks have absolute salience values. However, this contradicts the definition of landmarks as being salient in comparison to their surroundings. In this work-in-progress study, a probability-based soft classification approach is proposed to automatically select indoor landmarks. Specifically, we aggregated fundamental salience measures regarding visual, structural, and semantic dimensions from related studies to create an indoor landmark dataset. Then we, compared the performances of machine learning classifiers with several metrics and interpreted the local contributions of salience measures. Finally, we utilized a probability calibration technique that allows for finer-grained representations of indoor landmarks to include them in the route guidance process. According to the preliminary results of this study, boosting-based machine learning algorithms provide remarkable results, and functional uniqueness, category, and intensity measures are considered more important to select indoor landmarks. Moreover, our soft probability-based classification framework seems promising for selecting and representing landmarks in a fine-grained manner. However, the feasibility of the proposed framework should be further validated with user studies.
en
dc.language.iso
en
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
Indoor navigation
en
dc.subject
landmark
en
dc.subject
machine learning
en
dc.subject
Shapley Additive Explanations
en
dc.title
A Framework for Automatic Selection of Indoor Landmarks using Machine Learning Algorithms and Shapley Additive Explanations
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/5702
-
dc.contributor.affiliation
Yıldız Technical University, Turkey
-
dc.contributor.affiliation
Yıldız Technical University, Turkey
-
dc.relation.doi
10.34726/5400
-
dc.description.startpage
25
-
dc.description.endpage
30
-
dc.rights.holder
Authors
-
dc.type.category
Full-Paper Contribution
-
tuw.booktitle
Proceedings of the 18th International Conference on Location Based Services
-
tuw.relation.ispartof
10.34726/5400
-
tuw.researchTopic.id
I5
-
tuw.researchTopic.name
Visual Computing and Human-Centered Technology
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E000 - Technische Universität Wien
-
dc.identifier.libraryid
AC17203186
-
dc.description.numberOfPages
6
-
tuw.author.orcid
0000-0002-8763-5716
-
dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
tuw.event.name
18th International Conference on Location Based Services (LBS 2023)