CASE-BASED REASONING FOR IMAGE ANALYSIS AND INTERPRETATION

The development of image interpretation systems is concerned with tricky problems such as a limited number of observations, environmental influence, and noise. Recent systems lack robustness, accuracy, and flexibility. The introduction of case-based reasoning (CBR) strategies can help to overcome these drawbacks. The special type of information (i.e. images) and the problems mentioned above provide special requirements for CBR strategies. In this chapter we review what has been achieved so far and research topics concerned with case-based image analysis and interpretation.