Accurately and automatically detecting image orientation is a task of great importance in intelligent image processing. In this paper, we present an automatic image orientation detection algorithm based on low-level features: color moments; Harris corner; phase symmetry; edge direction histogram. Support vector machines, statistical classifiers, parzen window classifiers are used in our approach: we use Borda Count as combination rule for these classifiers. Large amounts of experiments have been conducted, on a database of more than 6000 images of real photos, to validate our approach. Discussions and future directions for this work are also addressed at the end of the paper. © 2005 Elsevier B.V. All rights reserved.
Lumini, A., & Nanni, L. (2006). Detector of image orientation based on Borda Count. Pattern Recognition Letters, 27(3), 180–186. https://doi.org/10.1016/j.patrec.2005.08.023