Semi-automatic data annotation guided by feature space projection

Autor(es)

Benato, Barbara C. ^[1] ; Gomes, Jancarlo F. ^[1] ; Telea, Alexandru C. ^[2] ; Falcao, Alexandre X. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, Campinas - Brazil

^[2] Univ Utrecht, Fac Sci, Dept Informat & Comp Sci, Utrecht - Netherlands

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION; v. 109, JAN 2021.
• Citações Web of Science: 0

Resumo

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. (C) 2020 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 16/25776-0 - Otimização de redes neurais Autocodificadoras guiada por análise visual de dados
• Beneficiário: Bárbara Caroline Benato
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 17/25327-3 - Análise visual para propagação de rótulo assistida pelo usuário no projeto de classificador de imagens baseado em redes neurais
• Beneficiário: Bárbara Caroline Benato
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Mestrado