Optimal Decision Tree Synthesis for Efficient Neighborhood Computation

This work proposes a general approach to optimize the time required to perform a choice in a decision support system, with particular reference to image processing tasks with neighborhood analysis. The decisions are encoded in a decision table paradigm that allows multiple equivalent procedures to be performed for the same situation. An automatic synthesis of the optimal decision tree is implemented in order to generate the most efficient order in which conditions should be considered to minimize the computational requirements. To test out approach, the connected component labeling scenario is considered. Results will show the speedup introduced using an automatically built decision system able to efficiently analyze and explore the neighborhood.