Automatic picture orientation detection based on classifier combination

Abstract

Automatic picture orientation recognition is of great significance in many applications such as consumer gallery management, webpage browsing, content-based searching or web printing. We try to solve this high-level classification problem by relatively low-level features including Spacial Color Moment (CM) and Edge Direction Histogram (EDH). An improved distance-based classification scheme is adopted as our classifier. We propose an input-vector-rotating strategy, which is computationally more efficient than several conventional schemes, instead of collecting and training samples for all four classes. Then we research on the classifier combination algorithm to make full use of the complementarity between different features and classifiers. Our classifier combination methods include two levels: feature-level and measurement-level. And we present two classifier combination structures (parallel and cascaded) at measurement-level with a rejection option. As the precondition of measurement-level methods, the theory of Classifier's Confidence Analysis (CCA) is introduced with the definition of concepts such as classifier's confidence and generalized confidence. The classification system finally approached 90% recognition accuracy on a wide unconstrained consumer picture set.