Publication Details
Clustering for Video Retrieval
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.
@inproceedings{BUT30758,
author="Petr {Chmelař} and Ivana {Burgetová} and Jaroslav {Zendulka}",
title="Clustering for Video Retrieval",
booktitle="Data Warehousing and Knowledge Discovery",
year="2009",
series="Lecture Notes in Computer Science",
volume="5691",
pages="390--401",
publisher="Springer Verlag",
address="Heidelberg",
isbn="978-3-642-03729-0"
}