Shot transition detection with FAM-based fuzzy inference

We describe a fuzzy inference approach for detecting and classifying shot transitions in video sequences. Our approach basically extends FAM (fuzzy associative memory) to detect and classify shot transitions, including cuts, fades and dissolves. We consider a set of feature values that characterize differences between two consecutive frames as input fuzzy sets, and the types of shot transitions as output fuzzy sets. An initial implementation runs at approximately 7 frames per second on PC and yields promising results.