VisualLabel: An integrated multimedia content management and access framework

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Tampere University
  • Lynx Technology Finland Oy
  • Nokia
  • Arcada University of Applied Sciences

Abstract

With the rapid growth of image and video data as well as the fast spread of user-generated content in social media and cloud services, it has become increasingly difficult for users to have efficient access and effective management of their digital content. In this paper we present a novel integrated open source multimedia content management and access framework, called VisualLabel, that enables smart photo services based on automated visual content analysis, annotation, search and retrieval using state of the art analysis back ends for services such as Facebook and Flickr. This paper includes detailed descriptions of the high-level architecture used in the VisualLabel framework and proof-of-concept implementations of a front-end service, along with three analysis back ends and a web client, all of which demonstrate the basic functionality provided by the framework.

Details

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXIX
EditorsNaofumi Yoshida, Chawan Koopipat, Yasushi Kiyoki, Petchporn Chawakitchareon, Aran Hansuebsai, Virach Sornlertlamvanich, Bernhard Thalheim, Hannu Jaakkola
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Modelling and Knowledge Bases - Krabi, Thailand
Duration: 5 Jun 20179 Jun 2017
Conference number: 27

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume301
ISSN (Print)0922-6389

Conference

ConferenceInternational Conference on Information Modelling and Knowledge Bases
Abbreviated titleEJC
CountryThailand
CityKrabi
Period05/06/201709/06/2017

    Research areas

  • CBIR, Content management, Framework, REST, Web client

ID: 32889438