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VIAL: a unified process for visual interactive labeling

Bernard, Jürgen and Zeppelzauer, Matthias and Sedlmair, Michael and Aigner, Wolfgang (2018):
VIAL: a unified process for visual interactive labeling.
In: The Visual Computer, Springer, pp. 1189-1207, 34, (9), ISSN 0178-2789,
DOI: 10.1007/s00371-018-1500-3,
[Online-Edition: https://doi.org/10.1007/s00371-018-1500-3],
[Article]

Abstract

The assignment of labels to data instances is a fundamental prerequisite for many machine learning tasks. Moreover, labeling is a frequently applied process in visual interactive analysis approaches and visual analytics. However, the strategies for creating labels usually differ between these two fields. This raises the question whether synergies between the different approaches can be attained. In this paper, we study the process of labeling data instances with the user in the loop, from both the machine learning and visual interactive perspective. Based on a review of differences and commonalities, we propose the "visual interactive labeling" (VIAL) process that unifies both approaches.We describe the six major steps of the process and discuss their specific challenges. Additionally, we present two heterogeneous usage scenarios from the novel VIAL perspective, one on metric distance learning and one on object detection in videos. Finally, we discuss general challenges to VIAL and point out necessary work for the realization of future VIAL approaches.

Item Type: Article
Erschienen: 2018
Creators: Bernard, Jürgen and Zeppelzauer, Matthias and Sedlmair, Michael and Aigner, Wolfgang
Title: VIAL: a unified process for visual interactive labeling
Language: English
Abstract:

The assignment of labels to data instances is a fundamental prerequisite for many machine learning tasks. Moreover, labeling is a frequently applied process in visual interactive analysis approaches and visual analytics. However, the strategies for creating labels usually differ between these two fields. This raises the question whether synergies between the different approaches can be attained. In this paper, we study the process of labeling data instances with the user in the loop, from both the machine learning and visual interactive perspective. Based on a review of differences and commonalities, we propose the "visual interactive labeling" (VIAL) process that unifies both approaches.We describe the six major steps of the process and discuss their specific challenges. Additionally, we present two heterogeneous usage scenarios from the novel VIAL perspective, one on metric distance learning and one on object detection in videos. Finally, we discuss general challenges to VIAL and point out necessary work for the realization of future VIAL approaches.

Journal or Publication Title: The Visual Computer
Volume: 34
Number: 9
Publisher: Springer
Uncontrolled Keywords: Information visualization, Visual analytics, Machine learning, Labeling, Active learning, Classifications, Similarity search
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 10 Jul 2019 11:33
DOI: 10.1007/s00371-018-1500-3
Official URL: https://doi.org/10.1007/s00371-018-1500-3
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