Publication entry:

Evaluating a Bayesian-like relevance feedback model with text-to-image search initialization

Full citation: Peška, L., Vomlelová, M., Veselý, P., Škrhák, V., & Lokoč, J. (2023). Evaluating a bayesian-like relevance feedback model with text-to-image search initialization. Multimedia Tools and Applications, 82(15), 22305–22341.
BibTeX citation file download: peska2023evaluating.bib

Internal authors

Ladislav Peška

Associate professor

Vít Škrhák

PhD student

Jakub Lokoč

Associate professor

Research topics

Multimedia Retrieval

Multimedia data penetrate all areas of our lives and become more important than ever. We meet them in social media applications, video streaming services, digital libraries as well as in specialized medical or industrial fields. As multimedia data are produced using sensors, their primary representation is semantically unstructured (e.g., an image is a bunch of pixels). Hence, recognition of what actually is inside a particular multimedia document and subsequent retrieval is a hard task that requires advanced techniques for feature extraction, object detection, similarity modeling, etc. Many of these techniques are based on machine-learning models. We carry out research in various multimedia retrieval problems and also propose many topics for student academic works (Bc, Mgr, PhD).