Transfer Boosting With Synthetic Instances for Class Imbalanced Object Recognition.

A challenging problem in object recognition is to train a robust classifier with small and imbalanced data set. In such cases, the learned classifier tends to overfit the training data and has low prediction accuracy on the minority class. In this paper, we address the problem of class imbalanced object recognition by combining synthetic minorities over-sampling technique (SMOTE) and instance-based transfer boosting to rebalance the skewed class distribution. We present ways of generating synthetic instances under the learning framework of transfer Adaboost. A novel weighted SMOTE technique (WSMOTE) is proposed to generate weighted synthetic instances with weighted source and target instances at each boosting round. Based on WSMOTE, we propose a novel class imbalanced transfer boosting algorithm called WSMOTE-TrAdaboost and experimentally demonstrate its effectiveness on four datasets (Office, Caltech256, SUN2012, and VOC2012) for object recognition application. Bag-of-words model with SURF features and histogram of oriented gradient features are separately used to represent an image. We experimentally demonstrated the effectiveness and robustness of our approaches by comparing it with several baseline algorithms in boosting family for class imbalanced learning.