A new generation of intelligent trainable tools for analyzing large scientific image databases

Abstract

The focus of this paper is on the detection of natural, as opposed to human-made, objects. The distinction is important because, in the context of image analysis, natural objects tend to possess much greater variability in appearance than human-made objects. Hence, we shall focus primarily on the use of algorithms that 'learn by example' as the basis for image exploration. The 'learn by example' approach is potentially more generally applicable compared to model-based vision methods since domain scientists find it relatively easier to provide examples of what they are searching for versus describing a model.