Active Learning for Multi-Label Image Annotation

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Title: Active Learning for Multi-Label Image Annotation
Authors: Singh, MohanCurran, EoinCunningham, Pádraig
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Date: Jan-2009
Online since: 2021-08-05T09:30:01Z
Abstract: Active learning is useful in situations where labeled data is scarce, unlabeled data is available and labeling has some cost associated with it. In such situations active learning helps by identifying a minimal set of items to label that will allow the training of an effective classifier. Thus active learning is appropriate for annotation tasks in multimedia, particularly in image labeling. In this paper we address the challenge of using active learning for multi-labeling of images in personal image collections. Multi-label learning covers situations where objects can have more than one class label and a learner is trained to assign multiple labels simultaneously. In this paper we report results on a learning system for labeling personal image collections that is both active and multi-label. The focus of the research has been to reduce the overall number of images that are presented to the user for labeling.
Type of material: Technical Report
Publisher: University College Dublin. School of Computer Science and Informatics
Series/Report no.: UCD CSI Technical Reports; ucd-csi-2009-1a
Copyright (published version): 2009 the Authors
Keywords: AnnotationImage taggingPersonal image collectionsMachine learning
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Language: en
Status of Item: Not peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:Computer Science and Informatics Technical Reports

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