Automatic image orientation detection with prior hierarchical content-based classification

This paper presents an algorithm for automatic detection of the orientation of user generated images. The images can initially be into 3 different orientations. The algorithm utilizes SVM classifier trained over feature vectors of the low-level characteristics of the images in the training set. In order to increase classification accuracy, prior to the SVM classification, the images are hierarchically pre-classified into different groups regarding to the semantic cues they contain, like presence and absence of sky, light, or human faces. Then separate SVM classifier is trained for each group. Also, the paper presents the conclusions of an online survey about the user preferences for software for automatic image orientation detection and gives explanation how those conclusions correspond to the accuracy of the proposed algorithm.