Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/89024
Type: Artigo de evento
Title: A Data Reduction And Organization Approach For Efficient Image Annotation
Author: Saito P.T.M.
De Rezende P.J.
Falcao A.X.
Suzuki C.T.N.
Gomes J.F.
Abstract: The labor-intensive and time-consuming process of annotating data is a serious bottleneck in many pattern recognition applications when handling massive datasets. Active learning strategies have been sought to reduce the cost on human annotation, by means of automatically selecting the most informative unlabeled samples for annotation. The critical issue lies on the selection of such samples. As an effective solution, we propose an active learning approach that preprocesses the dataset, efficiently reduces and organizes a learning set of samples and selects the most representative ones for human annotation. Experiments performed on real datasets show that the proposed approach requires only a few iterations to achieve high accuracy, keeping user involvement to a minimum. Copyright 2013 ACM.
Editor: 
Rights: fechado
Identifier DOI: 10.1145/2480362.2480374
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84877964844&partnerID=40&md5=5f67e88de3317992cb38516918639971
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

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