Impact of quasi-expertise on knowledge acquisition in computer vision

Ripple down rules (RDR)'s incremental knowledge acquisition provides computer vision applications with the ability to gradually adapt to the domain and circumvent some of its learning challenges. RDR use incremental exception-based theory revision and rely on the expert to provide the rule conditions. A computer vision expert whilst understanding their significance cannot always provide accurate rule conditions using numeric attributes. This work investigates the impact of the quasiexpertise of vision experts on the structure and performance of the acquired knowledge base. The findings provide insights into the design of features and strategies to facilitate the use of quasiexpertise for knowledge acquisition in computer vision.