Reliable Image Classification by Combining Features and Random Subspace Support Vector Machine Ensemble

We investigate the implementation of image categorization algorithms with a reject option, as a mean to enhance the system reliability and to attain a higher classification accuracy. A reject option is desired in many image-classification applications for which the system should abstain from making decisions on the most uncertain images. Based on the random subspace (RS) ensemble learning model, a highly reliable image classification scheme is proposed by applying RS support vector machine (SVM) ensemble. Being different to previous classifier ensembles which focus on increasing classification accuracy exclusively, the objective of the proposed SVM ensemble is to provide classification confidence and implement reject option to accommodate the situations where no decision should be made. The ensemble is created with four different feature descriptions, including local binary pattern (LBP), pyramid histogram of oriented gradient (PHOG), Gabor filtering and curvelet transform. The consensus degree from the ensemble's voting conforms to the confidence measure and the rejection option is accomplished accordingly when the confidence falls below a threshold. The reliable recognition scheme is empirically evaluated on three image categorization benchmark databases, including the face database created by Aleix Martinez and Robert Benavente (AR faces), a subset of Caltech-101 images for object classification, and 15 natural scene categories, all of which yielded consistently high reliable results, thus demonstrating the effectiveness of the proposed approach. For example, a 99.9% accuracy was obtained with a rejection rate of 2.5% for the AR faces, which exhibit promising potentials for real-world applications.