Restricted set classification: Who is there?. (March 2017) - Kuncheva, Ludmila I.; Rodríguez, Juan J.; Jackson, Aaron S.
Abstract: We consider a problem where a set X of N objects (instances) coming from c classes have to be classified simultaneously. A restriction is imposed on X in that the maximum possible number of objects from each class is known, hence we dubbed the problem who-is-there? We compare three approaches to this problem: (1) independent classification whereby each object is labelled in the class with the largest posterior probability; (2) a greedy approach which enforces the restriction; and (3) a theoretical approach which, in addition, maximises the likelihood of the label assignment, implemented through the Hungarian assignment algorithm. Our experimental study consists of two parts. The first part includes a custom-made chess data set where the pieces on the chess board must be recognised together from an image of the board. In the second part, we simulate the restricted set classification scenario using 96 datasets from a recently collated repository (University of Santiago de Compostela, USC). Our results show that the proposed approach (3) outperforms approaches (1) and (2). Abstract : Highlights: We define three new problems: who-is-who, who-is-missing and who-is-there. Related areas: object tracking, relaxation labelling, multi-instance classification. We propose a solution based on the Hungarian assignment algorithm. Our solution outperforms provably the original classifier and a greedy approach. Our real-life examples are naming the fish in a tank and labelling chess pieces.