Abstract
Consider a set-classification task where c objects must be labelled simultaneously in c classes, knowing that there is only one object coming from each class (full-class set). Such problems may occur in automatic attendance registration systems, simultaneous tracking of fast moving objects and more. A Bayes-optimal solution to the full-class set classification problem is proposed using a single classifier and the Hungarian assignment algorithm. The advantage of set classification over individually based classification is demonstrated both theoretically and experimentally, using simulated, benchmark and real data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have