Image orientation detection using LBP-based features and logistic regression
In this paper a method for digital image orientation recognition is proposed. Feature vectors are chosen to be flip-invariant to effectively classify images onto portrait-oriented and landscapeoriented. A new texture feature is proposed based on the observation that more textured areas are located usually in the lower part of the image. The method was implemented in software and tested using an image set containing various photo images.