An efficient data-scalable algorithm for image orientation detection

Image orientation detection is a useful, yet challenging research topic in intelligent image processing. Existing methods generally train a detector on ensemble data-set which is little scalability when new image samples with novel scenes come. This paper proposes a data-scalable algorithm for image orientation detection using bagging, a method aggregates several classifiers trained independently on non-intersecting sub data sets. By the proposed algorithm, when new classifiers trained on novel data sets are added, the prediction accuracy increases. In the paper, more representative feature set and more efficient learning algorithm are adopted to remedy the possible decrease of detection accuracy caused by the curtailment of the training data for single classifiers. Compared with previous work, the proposed algorithm has great competitiveness in terms of data-scalable ability, prediction accuracy, training and detection complexity.