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On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks
Joint Authors
Protopapadakis, Eftychios
Voulodimos, Athanasios
Doulamis, Anastasios
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-23
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
One of the most important aspects in semisupervised learning is training set creation among a limited amount of labeled data in such a way as to maximize the representational capability and efficacy of the learning framework.
In this paper, we scrutinize the effectiveness of different labeled sample selection approaches for training set creation, to be used in semisupervised learning approaches for complex visual pattern recognition problems.
We propose and explore a variety of combinatory sampling approaches that are based on sparse representative instances selection (SMRS), OPTICS algorithm, k-means clustering algorithm, and random selection.
These approaches are explored in the context of four semisupervised learning techniques, i.e., graph-based approaches (harmonic functions and anchor graph), low-density separation, and smoothness-based multiple regressors, and evaluated in two real-world challenging computer vision applications: image-based concrete defect recognition on tunnel surfaces and video-based activity recognition for industrial workflow monitoring.
American Psychological Association (APA)
Protopapadakis, Eftychios& Voulodimos, Athanasios& Doulamis, Anastasios. 2018. On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks. Complexity،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1135432
Modern Language Association (MLA)
Protopapadakis, Eftychios…[et al.]. On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks. Complexity No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1135432
American Medical Association (AMA)
Protopapadakis, Eftychios& Voulodimos, Athanasios& Doulamis, Anastasios. On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks. Complexity. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1135432
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135432