Handling Noisy Labels in Gaze-Based CBIR System View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-11-23

AUTHORS

Stéphanie Lopez , Arnaud Revel , Diane Lingrand , Frédéric Precioso

ABSTRACT

Handling noisy labels in classification is a core topic given the number of images available online with unprecise labels or even inaccurate ones. In our context, the label uncertainty is obtained by a fully gaze-based labelling process, called GBIE. We apply a noisy-label tolerant algorithm, P-SVM, which combines classification and regression processes. We have determined, among different strategies, a criterion of reliability to discriminate the most reliable labels involved in the classification from the most uncertain ones involved in the regression. The classification accuracy of the P-SVM is evaluated in different learning contexts, and can even compete in some cases with the baseline, i.e. a standard classification SVM trained with the true-class labels. More... »

PAGES

396-405

References to SciGraph publications

  • 2014. Visualizing and Understanding Convolutional Networks in COMPUTER VISION – ECCV 2014
  • 2015-06. Building effective SVM concept detectors from clickthrough data for large-scale image retrieval in INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
  • 1958-04. Pattern vision in young infants in THE PSYCHOLOGICAL RECORD
  • Book

    TITLE

    Advanced Concepts for Intelligent Vision Systems

    ISBN

    978-3-319-70352-7
    978-3-319-70353-4

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-70353-4_34

    DOI

    http://dx.doi.org/10.1007/978-3-319-70353-4_34

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092867528


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