Fusion Strategies for Large-Scale Multi-modal Image Retrieval

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00094968" target="_blank" >RIV/00216224:14330/17:00094968 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-662-55696-2_5" target="_blank" >http://dx.doi.org/10.1007/978-3-662-55696-2_5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-662-55696-2_5" target="_blank" >10.1007/978-3-662-55696-2_5</a>

Result language

angličtina

Original language name

Fusion Strategies for Large-Scale Multi-modal Image Retrieval

Original language description

Large-scale data management and retrieval in complex domains such as images, videos, or biometrical data remains one of the most important and challenging information processing tasks. Even after two decades of intensive research, many questions still remain to be answered before working tools become available for everyday use. In this work, we focus on the practical applicability of different multi-modal retrieval techniques. Multi-modal searching, which combines several complementary views on complex data objects, follows the human thinking process and represents a very promising retrieval paradigm. However, a rapid development of modality fusion techniques in several diverse directions and a lack of comparisons between individual approaches have resulted in a confusing situation when the applicability of individual solutions is unclear. Aiming at improving the research community’s comprehension of this topic, we analyze and systematically categorize existing multimodal search techniques, identify their strengths, and describe selected representatives. In the second part of the paper, we focus on the specific problem of large-scale multi-modal image retrieval on the web. We analyze the requirements of such task, implement several applicable fusion methods, and experimentally evaluate their performance in terms of both efficiency and effectiveness. The extensive experiments provide a unique comparison of diverse approaches to modality fusion in equal settings on two large real-world datasets.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA16-18889S" target="_blank" >GA16-18889S: Big Data Analytics for Unstructured Data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

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

Article name in the collection

Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIII

ISBN

9783662556955

ISSN

0302-9743

e-ISSN

—

Number of pages

39

Pages from-to

146-184

Publisher name

Springer

Place of publication

Berlin, Heidelberg

Event location

Berlin, Heidelberg

Event date

Jan 1, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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