Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||A Data Reduction And Organization Approach For Efficient Image Annotation|
De Rezende P.J.
|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.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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