Adaptive Content-based Image Retrieval using Semi-Supervised Learning Method

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Automatic image annotation is a hopeful sub-technique for image database retrieval. We have been constructing a generative model system for automatic image annotation using semi-supervised learning method. As it can be easily unstable for the higher dimensions, we must apply a dimensionality reduction method in advance. Generally, conventional supervised dimensionality reduction method (using labeled samples) suffers from the degenerate covariance matrix problem in the case of a small number of samples. On the other hand, unsupervised dimensionality reduction method (using unlabeled samples) can't recognize the differences among the categories properly. In this study, we propose a novel semi-supervised dimensionality reduction method using a small number of labeled samples and a large number of unlabeled samples. By the result of experiments, the classification rate of the proposed method was 5.1 points better than that of the unsupervised method.