Sciweavers

Share
ICASSP
2011
IEEE

Semi-supervised handwritten digit recognition using very few labeled data

8 years 12 months ago
Semi-supervised handwritten digit recognition using very few labeled data
We propose a novel semi-supervised classifier for handwritten digit recognition problems that is based on the assumption that any digit can be obtained as a slight transformation of another sufficiently close digit. Given a number of labeled and unlabeled images, it is possible to determine the class membership of each unlabeled image by creating a sequence of such image transformations that connect it, through other unlabeled images, to a labeled image. In order to measure the total transformation, a robust and reliable metric of the path length is proposed, which combines a local dissimilarity between consecutive images along the path with a global connectivity-based metric. For the local dissimilarity we use a symmetrized version of the zero-order image deformation model (IDM) proposed by Keysers et al. in [1]. For the global distance we use a connectivity-based metric proposed by Chapelle and Zien in [2]. Experimental results on the MNIST benchmark indicate that the proposed cla...
Steven Van Vaerenbergh, Ignacio Santamaría,
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Steven Van Vaerenbergh, Ignacio Santamaría, Paolo Emilio Barbano
Comments (0)
books