Abstract
This paper presents an incremental algorithm for image classification problems. Virtual labels are automatically formed by clustering in the output space. These virtual labels are used for the process of deriving discriminating features in the input space. This procedure is performed recursively in a coarse-to-fine fashion resulting in a tree, performing incremental hierarchical discriminating regression (IHDR). Embedded in the tree is a hierarchical probability distribution model used to prune unlikely cases. A sample size dependent negative-log-likelihood (NLL) metric is introduced to deal with large sample-size cases, small sample-size cases, and unbalanced sample-size cases, measured among different internal nodes of the IHDR algorithm. We report the experimental results of the proposed algorithm for an OCR classification problem and an image orientation classification problem.
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