Ing. Ivana Burgetová, Ph.D.

CHMELAŘ Petr, RUDOLFOVÁ Ivana a ZENDULKA Jaroslav. Clustering for Video Retrieval. In: Data Warehousing and Knowledge Discovery. Heidelberg: Springer Verlag, 2009, s. 390-401. ISBN 978-3-642-03729-0.
Jazyk publikace:angličtina
Název publikace:Clustering for Video Retrieval
Název (cs):Clustering for Video Retrieval
Sborník:Data Warehousing and Knowledge Discovery
Konference:20th International Conference on Database and Expert Systems Applications
Řada knih:LNCS 5691
Místo vydání:Heidelberg, DE
Vydavatel:Springer Verlag
Klíčová slova
Incremental clustering, MLD, Leader, ART, video retrieval, feature extraction, SURF, MSER, SIFT, cosine distance.
The paper deals with an application of clustering we used as one of data reduction methods included in processing huge amount of video data provided for TRECVid evaluations. The problem we solved by means of clustering was to partition the local feature descriptors space so that thousands of partitions represent visual words, which may be effectively employed in video retrieval using classical information retrieval techniques. It has proved that well-known algorithms as K-means do not work well in this task or their computational complexity is too high. Therefore we developed a simple clustering method (referred to as MLD) that partitions the high-dimensional feature space incrementally in one to two database scans. The paper describes the problem of video retrieval and the role of clustering in the process, the MLD method and experiments focused on comparison with other clustering methods in the video retrieval application context.
   author = {Petr Chmela{\v{r}} and Ivana Rudolfov{\'{a}} and
	Jaroslav Zendulka},
   title = {Clustering for Video Retrieval},
   pages = {390--401},
   booktitle = {Data Warehousing and Knowledge Discovery},
   series = {LNCS 5691},
   year = 2009,
   location = {Heidelberg, DE},
   publisher = {Springer Verlag},
   ISBN = {978-3-642-03729-0},
   language = {english},
   url = {}

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