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GAUK 910913
Real-time Exploration Queries in Multimedia Databases
2013 - 2015

Nowadays, the similarity search in multimedia databases is performed through similarity queries explicitly specified by users. The queries return a certain part of the database that is relevant to the user specified query parameters. However, this approach suffers in case the user does not know how to specify the query, or actually she/he only wants to know what the database contains in the whole picture. In such case non-standard access to data is more appropriate, e.g., the exploration of a multimedia database.
During the exploration process the user gains a complex idea of all the stored data rather than a particular part of database returned as the result of some similarity query. In the complex view the user is osupported in browsing the space of multimedia data (typically by multi-touch device, provided by modern technologies, e.g., iPad) and that results in stream of similarity queries. For a convenient user-friendly browsing, the exploration system has to evaluate these queries promptly, which is not guaranteed in case of standard query processing (even approximate). Hence, the goal of this project is to propose and implement access methods that provide functionality of real-time similarity retrieval, thus founding fundamentals for user-friendly exploration of multimedia databases.

Principal investigator : Juraj Mosko
Team member : Tomas Skopal, Tomas Bartos, Jakub Lokoc, Tomas Grosup
GAUK 567312
Algorithmic exploration of axiom spaces for efficient similarity search at large scale
2012 - 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.
Principal investigator : Tomas Bartos
Team member : Tomas Skopal, Juraj Mosko
GAČR P202/11/0968
Large-scale Nonmetric Similarity Search in Complex Domains
2011 - 2014

The similarity search is popular in various areas of computing, including multimedia databases, data mining, bioinformatics, etc. For a long time, the database approaches to similarity search assumed the similarity as a metric distance. Due to its properties, metric similarity allows to index a database such that it can be queried efficiently (quickly). However, together with the increasing complexity of data across various domains, there appeared many similarities in recent years that were not metrics (i.e., nonmetrics). The database research, however, is still not aware of the huge potential market for nonmetric similarity search, recognizing just the metric space model.

            This project aims to propose formal models followed by a design of access methods for efficient nonmetric similarity search, that is, search in databases where the similarity is not restricted by the metric postulates. Such a goal would bring an efficient database solution to the domain experts that need to pursue large-scale content-based retrieval tasks in complex databases, like multimedia retrieval, similarity-based data mining, complex pattern matching, classification and prediction in bioinformatics, etc.

Principal investigator : Tomas Skopal
Team member : David Hoksza, Jakub Lokoc, Jiri Novak, Juraj Mosko, Tomas Bartos