Weakly-supervised object localization in unlabeled image collection

摘要

Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images.