IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatic document orientation detection and categorization through document vectorization
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Integrated patch model: A generative model for image categorization based on feature selection
Pattern Recognition Letters
Image annotation: which approach for realistic databases?
Proceedings of the 6th ACM international conference on Image and video retrieval
What's up CAPTCHA?: a CAPTCHA based on image orientation
Proceedings of the 18th international conference on World wide web
Hierarchical System for Content Based Categorization and Orientation of Consumer Images
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Sketcha: a captcha based on line drawings of 3D models
Proceedings of the 19th international conference on World wide web
Fast and robust skew estimation of scanned documents through background area information
Pattern Recognition Letters
Musical slideshow: boosting user experience in photo presentation
Multimedia Tools and Applications
An algorithm for the automatic estimation of image orientation
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
LCMKL: latent-community and multi-kernel learning based image annotation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.