In this project we will develop large-scale hierarchical object class models that are based on the intuitive principle of compositionality for the purpose of visual retrieval.
The main focus of this project, however, will be on modeling and learning a larger number of visual object categories within a hierarchical compositional framework which will allow for a computationally efficient recognition, online object learning and semantic visual retrieval. This approach will enable continous learning of novel object categories through user interaction and autonomous indexing of object categories in image databases. It will also open up new views of computer-user interaction in terms of continuous user-in-the-loop based semantic queries and queries at different levels of detail which retain their semantic meaning.