Exploiting Unlabeled Data in Content-Based Image Retrieval

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A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “[113] proposes the semi-supervised active image retrieval (SSAIR) approach where the focus is on image retrieval.”
A Survey on Multi-view Learning (2013, background): “ at each stage to maximize the learning objective in active learning. Considering various kinds of visual features, such as color and texture features, as suﬃcient and uncorrelated views of an image, Zhou et al. (2004) and Cheng and Wang (2007) introduced a co-training algorithm to conduct relevance”
Introduction to Semi-Supervised Learning (2009, unknown): “, [85, 126, 132, 183, 203, 202, 213].”
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments (2016, methodology): “[37] also achieved significant performance improvements by combining co-testing and co-training methods in image retrieval compared to either co-testing or co-training retrieval method.”
Active Learning Literature Survey (2009, unknown): “Some example formulations of semi-supervised active learning include McCallum and Nigam (1998), Muslea et al. (2000), Zhu et al. (2003), Zhou et al. (2004), and Tur et al. (2005).”
Democratic co-learning (2004, methodology): “More recently, such an approach has been successfully applied to content-based image retrieval [19].”