Abstract
The task of automated classification is a highly active research field with great practical benefit over a number of problem domains. However, due to the factors such as lack of available training examples, large degrees of imbalance in the training set, or overlapping classes, the task of automated classification is rarely straightforward in practice. Methods that adequately compensate for such difficulties are required. The recently developed bridging algorithm does just this for problems in the field of short string text classification. The algorithm integrates a collection of background knowledge into the classification process. In this paper, we have shown how the bridging algorithm was redesigned so it can be applied to image data. We also demonstrated it is effective to overcome a range of difficulties in the classification process.
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Petersen, H., Poon, J. (2009). Reworking Bridging for Use within the Image Domain. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_101
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DOI: https://doi.org/10.1007/978-3-642-03767-2_101
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