Case Acquisition and Case Mining for Case-Based Object Recognition

Abstract

Model-based image recognition requires a general model of the object that should be detected in an image. In many applications such models are not known a-priori instead of they must be learnt from examples. Real world applications such as the recognition of biological objects in images cannot be solved by one general model but a lot of different models are necessary in order to handle the natural variations of the appearance of the objects of a certain class. Therefore we are talking about case-based object recognition. In this paper we describe how the shape of an object can be extracted from images and input into a case description. These acquired cases we mine for more general shapes so that at the end a case base of shapes can be constructed and applied for case-based object recognition.