Tong tackles large scale image annotation using user contributed annotation (tags etc.) provided from social media network; where scalability is achieved through the use of the GRID'5000 computation mresources. Zhou et al. identify relevant text terms from text blocks that surround the web images in order to improve the accuracy of web image annotation on a 5 million image dataset. Wang et al. propose a deep model-based and data-driven hybrid architecture for annotating images. It is shown that DMD can scale-up well, thanks to its sparse regularization and scalable supervised learning steps. Creating a corpus is both expensive and time consuming. Liu and Huet propose a technique to automatically augment the training set for concept detector refinement. Two kinds of information is used to select the training data, one is visual feature, where video shots with high confidence scores are selected, the other is tags, in which tags are used to filter out video shots not tagged with the concept. 
