Name:
Algorithmic exploration of axiom spaces for efficient similarity search at large scale
Start year:
2012
End year:
2014 Similarity search is becoming popular in even more disciplines, such as multimedia databases, bioinformatics, data mining, or social networks. The large-scale search engines for such data are mostly based on models involving low-level features and simple similarity functions. There also exist complex models employing local features and higher-level similarities which provide higher retrieval effectiveness. An application of complex models, however, is not feasible at large scale due to insufficient portfolio of indexing techniques enabling fast search.
The existing techniques assume the metric space model that is too restrictive. In this project we revisit assumptions which persist in the mainstream research of content-based retrieval. Leaving the traditional indexing paradigms such as the metric space model, our goal is to propose alternative methods for indexing that shall lead to high-performance similarity search. We intend to develop an algorithmic framework for exploration of axioms (analytical properties) useful for indexing that hold in a given complex similarity space but were not discovered so far. Consequently, the known axioms will be localized as a small subset within the universe of all axioms suitable for indexing. The discovery of new axioms valid in some similarity space might have a huge impact in the database community.