Detail publikace

Clustering for Video Retrieval

CHMELAŘ, P. BURGETOVÁ, I. ZENDULKA, J.

Originální název

Clustering for Video Retrieval

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Incremental clustering, MLD, Leader, ART, video retrieval, feature extraction, SURF, MSER, SIFT, cosine distance.

Autoři

CHMELAŘ, P.; BURGETOVÁ, I.; ZENDULKA, J.

Rok RIV

2009

Vydáno

1. 9. 2009

Nakladatel

Springer Verlag

Místo

Heidelberg

ISBN

978-3-642-03729-0

Kniha

Data Warehousing and Knowledge Discovery

Edice

Lecture Notes in Computer Science

Strany od

390

Strany do

401

Strany počet

12

BibTex

@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"
}