Incremental learning for Bayesian classification of images

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. In this paper, we develop an incremental learning paradigm for Bayesian classification of images. Under the Bayesian paradigm, the class-conditional densities are represented in terms of codebook vectors. Learning is thus incrementally updating these codebook vectors as new training data become available. The proposed learning scheme estimates the already learnt training samples from the existing codebook vectors and augments these to the new training set for re-training the classifier. The above paradigm is shown to yield good results on three complex image classification problems. A classifier trained incrementally has comparable accuracies to the one which is trained using the true training samples.