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.
CITATION STYLE
Lopez, S., Revel, A., Lingrand, D., & Precioso, F. (2017). Handling noisy labels in gaze-based CBIR system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10617 LNCS, pp. 396–405). Springer Verlag. https://doi.org/10.1007/978-3-319-70353-4_34
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