Active graph based semi-supervised learning using image matching: Application to handwritten digit recognition

• A robust active learning method.
• Ground truth creation is optimized (time and cost reduction).
• The combination of efficient image matching and label propagation.
• The method is robust in four different scripts.

With the availability of large amounts of documents and multimedia content to be classified, the creation of new databases with labeled examples is an expensive task. Efficient supervised classifiers often require large training databases that are not always immediately available. Active learning approaches solve this issue by querying an expert to set a label to particular instances. In this paper, we present a novel active learning strategy for the classification of handwritten digits. The proposed method is based on a k-nearest neighbor graph obtained with an image deformation model, which takes into account local deformations. During the active learning procedure, the user is first asked to label the vertices with the highest number of neighbors. Thus, the expert sets the label to the examples that are more likely to propagate their labels to a high number of close neighbors. Then, a label propagation function is performed to automatically label the examples. The procedure is repeated until all the images are labeled. We evaluate the performance of the method on four databases corresponding to different scripts (Latin, Bangla, Devnagari, and Oriya). We show that it is possible to label only 332 images in the MNIST training database to obtain an accuracy of 98.54% on this same database (60000 images). The robustness of the method is highlighted by the performance of handwritten digit recognition in different scripts.