The Multimedia Information Retrieval (MIR) research group of the NeMIS lab has a long experience in topics related to
Computer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions.
MultiMedia Information Retrieval (MMIR) is a research discipline of computer science that aims at extracting semantic information from multimedia data sources. Data sources include directly perceivable media such as audio, image and video, indirectly perceivable sources such as text, biosignals as well as not perceivable sources such as bioinformation, stock prices, etc.
Current data processing applications use data with considerably less structure and much less precise queries than traditional database systems. Examples are multimedia data like images or videos that offer query by example search, product catalogs that provide users with reference-based search, scientific data records from observations or experimental analyses such as biochemical and medical data, or XML documents that come from heterogeneous data sources on the Web or in intranets and thus does not exhibit a global schema. Such data can neither be ordered in a canonical manner nor meaningfully searched by precise database queries that would return exact matches. This novel situation is what has given rise to similarity searching, also referred to as content-based or similarity retrieval.