Approximating adaptive distance measures using scalable feature signatures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10313495" target="_blank" >RIV/00216208:11320/15:10313495 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11042-014-2251-4" target="_blank" >http://dx.doi.org/10.1007/s11042-014-2251-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-014-2251-4" target="_blank" >10.1007/s11042-014-2251-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Approximating adaptive distance measures using scalable feature signatures

Popis výsledku v původním jazyce

The feature signatures in connection with the adaptive distance measures have become a respected similarity model for effective multimedia retrieval. However, the efficiency of the model is still a challenging task because the adaptive distance measureshave at least quadratic time complexity according to the number of tuples in feature signatures. In order to reduce the number of tuples in feature signatures, we introduce the scalable feature signatures, a new formal framework enabling definition of new methods based on agglomerative hierarchical clustering. We show the framework can be used to express nontrivial feature signature reduction techniques including also popular agglomerative hierarchical clustering techniques. We experimentally demonstrate our new feature signature reduction techniques can be used to implement order of magnitude faster yet effective filter distances approximating the original adaptive distance measures. We also show the filter distances using our new feat

Název v anglickém jazyce

Approximating adaptive distance measures using scalable feature signatures

Popis výsledku anglicky

The feature signatures in connection with the adaptive distance measures have become a respected similarity model for effective multimedia retrieval. However, the efficiency of the model is still a challenging task because the adaptive distance measureshave at least quadratic time complexity according to the number of tuples in feature signatures. In order to reduce the number of tuples in feature signatures, we introduce the scalable feature signatures, a new formal framework enabling definition of new methods based on agglomerative hierarchical clustering. We show the framework can be used to express nontrivial feature signature reduction techniques including also popular agglomerative hierarchical clustering techniques. We experimentally demonstrate our new feature signature reduction techniques can be used to implement order of magnitude faster yet effective filter distances approximating the original adaptive distance measures. We also show the filter distances using our new feat

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GPP202%2F12%2FP297" target="_blank" >GPP202/12/P297: Synergické modelování adaptivních podobnostních měr pro potřeby multimediálního vyhledávání</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

—

Svazek periodika

2015/74

Číslo periodika v rámci svazku

December 2015

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

26

Strana od-do

11569-11594

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84947616854