Active learning through notes data in Flickr: an effortless training data acquisition approach for object localization

Most of the state-of-the-art systems for object localization rely on supervised machine learning techniques, and are thus limited by the lack of labeled training data. In this paper, our motivation is to provide training dataset for object localization effectively and efficiently. We argue that the notes data in Flickr can be exploited as a novel source for object modeling. At first, we apply a text mining method to gather semantically related images for a specific class. Then a handful of images are selected manually as seed images or initial training set. At last, the training set is expanded by an incremental active learning framework. Our approach requires significantly less manual supervision compared to standard methods. The experimental results on the PASCAL VOC 2007 and NUS-WIDE datasets show that the training data acquired by our approach can complement or even substitute conventional training data for object localization.