Automatic image orientation detection
We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a vector quantizer can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how feature clustering can be used as a feature selection mechanism to remove redundancies in the high-dimensional feature vectors used for classification. Experiments on a database of 17,901 images have shown that our proposed algorithm achieves an accuracy of approximately 97% on the training set and over 89% on an independent test set.