Large Scale Visual Semantic Extraction


Image annotation is the task of providing textual semantic to new images, by ranking a large set of possible annotations according to how they correspond to a given image. In the large scale setting, there could be millions of images to process and hundreds of thousands of potential distinct annotations. In order to achieve such a task we propose to build a so-called "embedding space", into which both images and annotations can be automatically projected. In such a space, one can then find the nearest annotations to a given image, or annotations similar to a given annotation. One can even build a visio-semantic tree from these annotations, that corresponds to how concepts (annotations) are similar to each other with respect to their visual characteristics. Such a tree will be different from semantic-only trees, such as WordNet, which do not take into account the visual appearance of concepts.