Publication entry:

Visualizations for universal deep-feature representations: survey and taxonomy

Full citation: Skopal, T., Peška, L., Hoksza, D., Sixtová, I., & Bernhauer, D. (2024). Visualizations for universal deep-feature representations: Survey and taxonomy. Knowledge and Information Systems, 66(2), 811–840.
BibTeX citation file download: skopal2024visualizations.bib

Internal authors

Tomáš Skopal

Deputy head of department, Professor

Ladislav Peška

Associate professor

David Hoksza

Head of department, Associate professor

Ivana Sixtová

PhD student

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).