Semi-automatic data annotation guided by feature space projection. (January 2021)

• Record Type: Journal Article
• Authors: Benato, Bárbara C.; Gomes, Jancarlo F.; Telea, Alexandru C.; Falcão, Alexandre X.
• Is Part Of: Pattern recognition. Volume 109(2021)
• Publication Date: 2021-01
• Subjects: Semi-supervised learning -- Unsupervised feature learning -- Interactive data annotation -- Autoencoder-neural networks -- Data visualization; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107612
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Feature space projections to increase semi-supervised learning. Data annotation using feature space projections outperforms automatic methods. Combining automatic and user-driven labeling methods improves annotation and classification results. Confidence measures reduce human labeling effort as compared to fully-manual labeling. Graphical abstract: Abstract: Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.