Semantics and feature discovery via confidence-based ensemble

Providing accurate and scalable solutions to map low-level perceptual features to high-level semantics is essential for multimedia information organization and retrieval. In this paper, we propose a confidence-based dynamic ensemble (CDE) to overcome the shortcomings of the traditional static classifiers. In contrast to the traditional models, CDE can make dynamic adjustments to accommodate new semantics, to assist the discovery of useful low-level features, and to improve class-prediction accuracy. We depict two key components of CDE: a multi-level function that asserts class-prediction confidence, and the dynamic ensemble method based upon the confidence function. Through theoretical analysis and empirical study, we demonstrate that CDE is effective in annotating large-scale, real-world image datasets.