title: Automatic image orientation detection.
abstract: 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 learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.
authors.affiliation: Agilent Technologies, Palo Alto, CA 94303-0867, USA. aditya_vailaya@agilent.com
authors.forename: Aditya
authors.initials: A
authors.lastname: Vailaya
authors.forename: HongJiang
authors.initials: H
authors.lastname: Zhang
authors.forename: Changjiang
authors.initials: C
authors.lastname: Yang
authors.forename: Feng-I
authors.initials: FI
authors.lastname: Liu
authors.forename: Anil K
authors.initials: AK
authors.lastname: Jain