Image labeling via incremental model learning

The well-built dataset is a pre-requisite for object categorization. The processes of collecting and labeling the images are laborious and monotonous. In order to label images efficiently, we propose an incremental learning model to label images automatically with a bounding box for each visual object category. Our approach combines image classification and object detection. Given a query image, our approach firstly searches the candidate regions coarsely using the beyond sliding windows scheme, and then locate the object finely using the sliding window scheme, and after that update the learning model. We use two criteria to evaluate the image labeling, the detection precision and the detection consistency with the ground truth label. Our approach can localize the object fast and sequentially update the learning model with the increasing of the unlabeled samples. The experiment results have demonstrated that our approach outperforms the BOW model in terms of the precision and consistency of detection.