Automatic image orientation detection Export

@article{citeulike:937986, 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.}, author = {Vailaya, A. and Zhang, H. and Yang, Changjiang and Liu, Feng-I and Jain, A. K.}, citeulike-article-id = {937986}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TIP.2002.801590}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1021081}, doi = {10.1109/TIP.2002.801590}, journal = {Image Processing, IEEE Transactions on}, keywords = {imageclassification, svm}, number = {7}, pages = {746--755}, posted-at = {2008-07-28 04:50:54}, priority = {2}, title = {Automatic image orientation detection}, url = {http://dx.doi.org/10.1109/TIP.2002.801590}, volume = {11}, year = {2002} }

TY - JOUR ID - citeulike:937986 L3 - citeulike-article-id:937986 N2 - 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. IS - 7 JF - Image Processing, IEEE Transactions on EP - 755 TI - Automatic image orientation detection VL - 11 SP - 746 KW - imageclassification KW - svm AU - Vailaya, A AU - Zhang, H AU - Yang, Changjiang AU - Liu, Feng-I AU - Jain, AK PY - 2002/// UR - http://dx.doi.org/10.1109/TIP.2002.801590 ER -