Compact Binary Feature for Open Set Recognition

Abstract

Most pattern recognition systems are closed set recognition systems in which any input sample is to be classified as belonging to one of the given classes. This paper, however, addresses the open set recognition problem in which a test sample may either come from one of the labeled or come from an unknown class. The number of unknown classes could potentially be unlimited. A compact binary feature (CBF) generated by an ensemble binary classifier (EBC) is proposed to solve the open set recognition problem. This method can be regarded as a type of ECOC (Error-Correcting Output Codes) combined with modern CNN (Convolutional Neural Network) techniques and adapted for open set recognition. By randomly partitioning the known classes into two groups and training a binary classifier with CNN to separate them apart, and by repeating such a procedure for many times, rich information is extracted from the training set in the form of an EBC which can associate any test sample with a CBF that can be matched according to Hamming distance which is very efficient to compute. According to the experiments on the Dunhuang ancient Chinese character dataset, EBC can boost the recognition performance significantly compared with a single feedforward CNN. Apart from that, CBF is very efficient for storage and saves lots of time in feature matching at the cost of more computation in the training phase.