Singh, Mohan

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Singh, Mohan
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Singh, Mohan

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  • Publication
    Active Learning for Multi-Label Image Annotation
    (University College Dublin. School of Computer Science and Informatics, 2009-01)
    Singh, Mohan 
    ;
    Curran, Eoin 
    ;
    Cunningham, Pádraig 
    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.
      171
  • Publication
    Score Normalization and Aggregation for Active Learning in Multi-label Classification
    (University College Dublin. School of Computer Science and Informatics, 2010-02)
    Singh, Mohan 
    ;
    Brew, Anthony 
    ;
    Greene, Derek 
    ;
    Cunningham, Pádraig 
    Active learning is useful in situations where labeled data is scarce, unlabeled data is available, and labeling a large number of examples is costly or impractical. These techniques help by identifying a minimal set of examples to label that will support the training of an effective classifier. Thus active learning is particularly relevant for the automation of annotation tasks in multimedia. In this paper we consider the problem of employing active learning for the assignment of multiple annotations or “tags” to images in personal image collections. This form of multi-label classification has received a lot of attention in recent years, however active multi-label classification is still a new research area. The main challenge in active multilabel classification is the selection of unlabeled examples that will be informative for all tags under consideration. This selection task proves surprisingly difficult primarily because of the paucity of labeled data available. In this paper we present some solutions to this problem based on aggregated rankings from classifiers for individual tags.
      106