Learning from the uncertain: leveraging social communities to generate reliable training data for visual concept detection tasks

Recent advances for visual concept detection based on deep convolutional neural networks have only been successful because of the availability of huge training datasets provided by benchmarking initiatives such as ImageNet. Assembly of reliably annotated training data still is a largely manual effort and can only be approached efficiently as crowd-working tasks. On the other hand, user generated photos and annotations are available at almost no costs in social photo communities such as Flickr. Leveraging the information available in these communities may help to extend existing datasets as well as to create new ones for completely different classification scenarios. However, user generated annotations of photos are known to be incomplete, subjective and do not necessarily relate to the depicted content. In this paper, we therefore present an approach to reliably identify photos relevant for a given visual concept category. We have downloaded additional metadata for 1 million Flickr images and have trained a language model based on user generated annotations. Relevance estimation is based on accordance of an image's annotation data with our language model and on subsequent visual re-ranking. Experimental results demonstrate the potential of the proposed method -- comparison with a baseline approach based on single tag matching shows significant improvements.