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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\u2010means clustering algorithm, and random selection. These approaches are explored in the context of four semisupervised learning techniques, i.e., graph\u2010based approaches (harmonic functions and anchor graph), low\u2010density separation, and smoothness\u2010based multiple regressors, and evaluated in two real\u2010world challenging computer vision applications: image\u2010based concrete defect recognition on tunnel surfaces and video\u2010based activity recognition for industrial workflow monitoring.<\/jats:p>","DOI":"10.1155\/2018\/6531203","type":"journal-article","created":{"date-parts":[[2018,9,23]],"date-time":"2018-09-23T23:31:06Z","timestamp":1537745466000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks"],"prefix":"10.1155","volume":"2018","author":[{"given":"Eftychios","family":"Protopapadakis","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0632-9769","authenticated-orcid":false,"given":"Athanasios","family":"Voulodimos","sequence":"additional","affiliation":[]},{"given":"Anastasios","family":"Doulamis","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2018,9,23]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-013-0183-3"},{"key":"e_1_2_10_2_2","doi-asserted-by":"crossref","unstructured":"ProtopapadakisE. 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