M
Markus Koskela
Researcher at Aalto University
Publications - 85
Citations - 2235
Markus Koskela is an academic researcher from Aalto University. The author has contributed to research in topics: Image retrieval & Content-based image retrieval. The author has an hindex of 22, co-authored 85 publications receiving 2155 citations. Previous affiliations of Markus Koskela include Helsinki University of Technology & Dublin City University.
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Book ChapterDOI
The 2005 PASCAL visual object classes challenge
Mark Everingham,Andrew Zisserman,Christopher Williams,Luc Van Gool,Moray Allan,Christopher M. Bishop,Olivier Chapelle,Navneet Dalal,Thomas Deselaers,Gyuri Dorkó,Stefan Duffner,J Eichhorn,Jason Farquhar,Mario Fritz,Christophe Garcia,Tom Griffiths,Frédéric Jurie,Daniel Keysers,Markus Koskela,Jorma Laaksonen,Diane Larlus,Bastian Leibe,Hongying Meng,Hermann Ney,Bernt Schiele,Cordelia Schmid,Edgar Seemann,John Shawe-Taylor,Amos Storkey,Sandor Szedmak,Bill Triggs,Ilkay Ulusoy,Ville Viitaniemi,Jianguo Zhang +33 more
TL;DR: The PASCAL Visual Object Classes Challenge (PASCALVOC) as mentioned in this paper was held from February to March 2005 to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects).
Journal ArticleDOI
PicSOM—content-based image retrieval with self-organizing maps
TL;DR: A novel system for content-based image retrieval in large, unannotated databases based on tree structured self-organizing maps (TS-SOMs), which implements a relevance feedback technique on content- based image retrieval.
Journal ArticleDOI
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
TL;DR: The results of the experiments show that the MPEG-7-defined content descriptors can be used as such in thePicSOM system even though Euclidean distance calculation, inherently used in the PicSom system, is not optimal for all of them.
Journal ArticleDOI
Self-Organising Maps as a Relevance Feedback Technique in Content-Based Image Retrieval
TL;DR: The PicSOM CBIR system is introduced, and the use of self-Organising Maps as a relevance feedback technique in it is described, analysed qualitatively, and visualised.
Proceedings ArticleDOI
PicSOM: self-organizing maps for content-based image retrieval
TL;DR: The image retrieval system, named PicSOM, can be seen as a SOM-based approach to relevance feedback which is a form of supervised learning to adjust the subsequent queries based on the user's responses during the information retrieval session.