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Fast online incremental transfer learning for unseen object classification using self-organizing incremental neural networks

Classifying new unseen object classes has become a popular topic of research in the computer-vision and robotics community. Coping with this problem requires determining the attributes shared among objects and transferring them for use in classifying unseen object classes. Nevertheless, most current state-of-the-art methods require a fully offline training process and take a very long time for the batch training process, which renders them inapplicable for use in online applications such as robotics. This study proposes a novel online and incremental approach for learning and transferring the learned attributes in order to classify another disjoint set of image classes. Among three methods proposed in this paper, a method combining those favorable features of a self-organizing incremental neural network (SOINN) and a support vector machine (SVM) achieves the best performance. This method, called the Alt-SOINN-SVM, can run online incrementally, similar to an SOINN, and perform accurate classification, similar to an SVM. An evaluation was performed with 50 classes of an animal with an attributes dataset (>30,000 images). The results shows that despite the great reduction in both learning time (92.25% reduction) and classification time (99.87% reduction), and possessing the ability for incremental learning on gradually obtained samples, the proposed method offers reasonably good accuracy for classification. Furthermore, the proposed methods are applicable to use with the increasing number of attribute which improves the accuracy gradually and incrementally.