Restricted Set Classification with prior probabilities: A case study on chessboard recognition.

Abstract In the Restricted Set Classification approach (RSC), a set of instances must be labelled simultaneously into a given number of classes, while observing an upper limit on the number of instances from each class. In this study we expand RSC by incorporating prior probabilities for the classes and demonstrate the improvement on the classification accuracy by doing so. As a case-study, we chose the challenging task of recognising the pieces on a chessboard from top-view images, without any previous knowledge of the game. This task fits elegantly into the RSC approach as the number of pieces on the board is limited, and each class (type of piece) may have only a fixed number of instances. We prepared an image dataset by sampling from existing competition games, arranging the pieces on the chessboard, and taking top-view snapshots. Using the grey-level intensities of each square as features, we applied single and ensemble classifiers within the RSC approach. Our results demonstrate that including prior probabilities calculated from existing chess games improves the RSC classification accuracy, which, in its own accord, is better than the accuracy of the classifier applied independently.