Accurate and automatic image orientation detection is of great importance in image libraries. We present automatic image orientation detection algorithms by adopting both the illuminance (structural) and chrominance (color) low-level content features. Statistical learning support vector machines (SVMs) are used in our approach as the classifiers. The different sources of the extracted image features, as well as the binary classification nature of SVM, require our system to be able to integrate the outputs from multiple classifiers. Both static combiner (averaging) and trainable combiner (also based on SVMs) are proposed and evaluated. In addition, two rejection options (regular and reinforced ambiguity rejections) are employed to improve orientation detection accuracy by sieving out images with low confidence values during the classification. A number of experiments on a database of more than 14000 images were performed to validate our approaches.