Similarity Query Postprocessing by Ranking

D 2012

Similarity Query Postprocessing by Ranking

BUDÍKOVÁ, Petra, Michal BATKO and Pavel ZEZULA

Basic information

Original name

Similarity Query Postprocessing by Ranking

Name in Czech

Přeuspořádání výsledků podobnostních dotazů

Authors

BUDÍKOVÁ, Petra (203 Czech Republic, guarantor, belonging to the institution), Michal BATKO (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

Revised Selected Papers. Berlin, Adaptive Multimedia Retrieval. Context, Exploration, and Fusion, LNCS 6817, p. 159-173, 15 pp. 2012

Publisher

Springer-Verlag

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Austria

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/12:00057261

Organization unit

Faculty of Informatics

ISBN

978-3-642-27168-7

ISSN

DOI

http://dx.doi.org/10.1007/978-3-642-27169-4_12

UT WoS

000306440900012

Keywords in English

ranking; content-based retrieval; metric space

Abstract

V originále

Current multimedia search technology is, especially in commercial applications, heavily based on text annotations. However, there are many applications such as image hosting web sites (e.g. Flickr or Picasa) where the text metadata are of poor quality in general. Searching such collections only by text gives usually rather unsatisfactory results. On the other hand, multimedia retrieval systems based purely on content can retrieve visually similar results but lag behind with the ability to grasp the semantics expressed by text annotations. In this paper, we propose various ranking techniques that can be transparently applied on any content-based retrieval system in order to improve the search results quality and user satisfaction. We demonstrate the usefulness of the approach on two large real-life datasets indexed by the MUFIN system. The improvement of the ranked results was evaluated by real users using an online survey.

Links

GA201/09/0683, research and development project

Name: Vyhledávání v rozsáhlých multimediálních databázích

Investor: Czech Science Foundation, Similarity Searching in Very Large Multimedia Databases

GP201/08/P507, research and development project

Name: Komplexní podobnostní dotazy nad rozsáhlými objemy dat

Investor: Czech Science Foundation, Complex similarity searching in very large data collections

VF20102014004, research and development project

Name: Multimediální analýza (Acronym: Multimediální analýza)

Investor: Ministry of the Interior of the CR

Citovat

BUDÍKOVÁ, Petra, Michal BATKO and Pavel ZEZULA. Similarity Query Postprocessing by Ranking. In M. Detyniecki, P. Knees, A. Nurnberger, M. Schedl, and S. Stober. Adaptive Multimedia Retrieval. Context, Exploration, and Fusion, LNCS 6817. Revised Selected Papers. Berlin: Springer-Verlag, 2012, p. 159-173. ISBN 978-3-642-27168-7. Available from: https://dx.doi.org/10.1007/978-3-642-27169-4_12.

@inproceedings{970312,
   author = {Budíková, Petra and Batko, Michal and Zezula, Pavel},
   address = {Berlin},
   booktitle = {Adaptive Multimedia Retrieval. Context, Exploration, and Fusion, LNCS 6817},
   doi = {http://dx.doi.org/10.1007/978-3-642-27169-4_12},
   edition = {Revised Selected Papers},
   editor = {M. Detyniecki, P. Knees, A. Nurnberger, M. Schedl, and S. Stober},
   keywords = {ranking; content-based retrieval; metric space},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Berlin},
   isbn = {978-3-642-27168-7},
   pages = {159-173},
   publisher = {Springer-Verlag},
   title = {Similarity Query Postprocessing by Ranking},
   year = {2012}
}

TY  - JOUR
ID  - 970312
AU  - Budíková, Petra - Batko, Michal - Zezula, Pavel
PY  - 2012
TI  - Similarity Query Postprocessing by Ranking
PB  - Springer-Verlag
CY  - Berlin
SN  - 9783642271687
KW  - ranking
KW  - content-based retrieval
KW  - metric space
N2  - Current multimedia search technology is, especially in commercial applications, heavily based on text annotations. However, there are many applications such as image hosting web sites (e.g. Flickr or Picasa) where the text metadata are of poor quality in general. Searching such collections only by text gives usually rather unsatisfactory results. On the other hand, multimedia retrieval systems based purely on content can retrieve visually similar results but lag behind with the ability to grasp the semantics expressed by text annotations. In this paper, we propose various ranking techniques that can be transparently applied on any content-based retrieval system in order to improve the search results quality and user satisfaction. We demonstrate the usefulness of the approach on two large real-life datasets indexed by the MUFIN system. The improvement of the ranked results was evaluated by real users using an online survey.
ER  -

BUDÍKOVÁ, Petra, Michal BATKO and Pavel ZEZULA. Similarity Query Postprocessing by Ranking. In M. Detyniecki, P. Knees, A. Nurnberger, M. Schedl, and S. Stober. \textit{Adaptive Multimedia Retrieval. Context, Exploration, and Fusion, LNCS 6817}. Revised Selected Papers. Berlin: Springer-Verlag, 2012, p.~159-173. ISBN~978-3-642-27168-7. Available from: https://dx.doi.org/10.1007/978-3-642-27169-4\_{}12.

Detailed Information on Publication Record