thesisInformation
- Publication Type: Master Thesis
- Workgroup(s)/Project(s): not specified
- Date: 2024
- Second Supervisor: Matthias Zeppelzauer
- Open Access: yes
- First Supervisor: Manuela Waldner
- Pages: 101
- Keywords: Human-centered computing, Visual analytics, User interface design
Abstract
Human annotation of image data is relevant for supervised machine learning, where labeled datasets are essential for training models. Traditionally, reducing the labeling effort was achieved through active learning, where the optimal next instance for labeling is selected by some heuristic to maximize utility. More recent work has focused on integrating user initiative in the labeling process through visual interactive labeling to steer the labeling process. This thesis proposes cVIL, a class-centric approach for visual interactive labeling that simplifies the human annotation process for large and complex image datasets. Previously, visual labeling approaches were typically instance-based, where the system visualizes individual instances for the user to label. cVIL utilizes diverse property measures to enable the labeling of difficult instances individually and in batches to label simpler cases rapidly. Since the property measures express the properties of an instance using a single scalar value, the visualizations are simple and scalable. cVIL combines the heuristic guidance approach of active learning with the user-centered approach of visual interactive labeling. In simulations, we could show that property measures can facilitate effective instance and batch labeling. In a user study, cVIL demonstrated superior accuracy and user satisfaction compared to the conventional instance-based visual interactive labeling approach that employs scatterplots. Participants also needed less time to complete the assigned tasks in cVIL compared to the baseline.Additional Files and Images
Weblinks
BibTeX
@mastersthesis{matt-2024-cvi,
title = "Class-Centric Visual Interactive Labeling using Property
Measures",
author = "Matthias Matt",
year = "2024",
abstract = "Human annotation of image data is relevant for supervised
machine learning, where labeled datasets are essential for
training models. Traditionally, reducing the labeling effort
was achieved through active learning, where the optimal next
instance for labeling is selected by some heuristic to
maximize utility. More recent work has focused on
integrating user initiative in the labeling process through
visual interactive labeling to steer the labeling process.
This thesis proposes cVIL, a class-centric approach for
visual interactive labeling that simplifies the human
annotation process for large and complex image datasets.
Previously, visual labeling approaches were typically
instance-based, where the system visualizes individual
instances for the user to label. cVIL utilizes diverse
property measures to enable the labeling of difficult
instances individually and in batches to label simpler cases
rapidly. Since the property measures express the properties
of an instance using a single scalar value, the
visualizations are simple and scalable. cVIL combines the
heuristic guidance approach of active learning with the
user-centered approach of visual interactive labeling. In
simulations, we could show that property measures can
facilitate effective instance and batch labeling. In a user
study, cVIL demonstrated superior accuracy and user
satisfaction compared to the conventional instance-based
visual interactive labeling approach that employs
scatterplots. Participants also needed less time to complete
the assigned tasks in cVIL compared to the baseline.",
pages = "101",
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Research Unit of Computer Graphics, Institute of Visual
Computing and Human-Centered Technology, Faculty of
Informatics, TU Wien",
keywords = "Human-centered computing, Visual analytics, User interface
design",
URL = "https://www.cg.tuwien.ac.at/research/publications/2024/matt-2024-cvi/",
}