Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition
•We propose a fast and accurate semi-automatic labeling strategy.•Human expert knowledge (time and cost) is reduced to minimum.•Unsupervised clustering and voting mechanism decide for the labels.•The method is generic, can be applied to other type of data. For training supervised classifiers to reco...
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| Published in: | Pattern recognition letters 2015-06, Vol.58, p.23-28 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | •We propose a fast and accurate semi-automatic labeling strategy.•Human expert knowledge (time and cost) is reduced to minimum.•Unsupervised clustering and voting mechanism decide for the labels.•The method is generic, can be applied to other type of data.
For training supervised classifiers to recognize different patterns, large data collections with accurate labels are necessary. In this paper, we propose a generic, semi-automatic labeling technique for large handwritten character collections. In order to speed up the creation of a large scale ground truth, the method combines unsupervised clustering and minimal expert knowledge. To exploit the potential discriminant complementarities across features, each character is projected into five different feature spaces. After clustering the images in each feature space, the human expert labels the cluster centers. Each data point inherits the label of its cluster’s center. A majority (or unanimity) vote decides the label of each character image. The amount of human involvement (labeling) is strictly controlled by the number of clusters – produced by the chosen clustering approach. To test the efficiency of the proposed approach, we have compared, and evaluated three state-of-the art clustering methods (k-means, self-organizing maps, and growing neural gas) on the MNIST digit data set, and a Lampung Indonesian character data set, respectively. Considering a k-nn classifier, we show that labeling manually only 1.3% (MNIST), and 3.2% (Lampung) of the training data, provides the same range of performance than a completely labeled data set would. |
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| ISSN: | 0167-8655 1872-7344 |