Semi-Supervised Object Recognition Based on Connected Image Transformations
| Title | Semi-Supervised Object Recognition Based on Connected Image Transformations |
| Publication Type | Journal Article |
| Year of Publication | 2013 |
| Authors | Van Vaerenbergh, S., I. Santamaría, and P E. Barbano |
| Journal | Expert Systems with Applications |
| Volume | 40 |
| Pagination | 7069--7079 |
| Month Published | December |
| ISSN | 0957-4174 |
| Abstract | We present a novel semi-supervised classifier model based on paths between unlabeled and labeled data through a sequence of local pattern transformations. A reliable measure of path-length is proposed that combines a local dissimilarity measure between consecutive patters along a path with a global, connectivity-based metric. We apply this model to problems of object recognition, for which we propose a practical classification algorithm based on sequences of "Connected Image Transformations" (CIT). Experimental results on four popular image benchmarks demonstrate how the proposed CIT classifier outperforms state-of-the-art semi-supervised techniques. The results are particularly significant when only a very small number of labeled patterns is available: the proposed algorithm obtains a generalization error of 4.57% on the MNIST data set trained on 2000 randomly chosen patterns with only 10 labeled patterns per digit class. |
| DOI | 10.1016/j.eswa.2013.06.029 |
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