Inexpensive Fusion Methods for Enhancing Feature Detection
In this paper we present two fusion methods for the task of high-level feature detection in multimedia content. Successful approaches to high-level feature detection typically leverage the techniques learned from Machine Learning utilized through ensemble architectures to achieve strong performance. However these approaches whilst successful are computationally expensive, and depending on the task require the use of significant computational resources. We propose two fusion methods that aim to combine the output of an initial basic machine learning approach with a lower-quality information source in order to gain diversity in the classified results whilst only requiring modest computing resources.