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Content-Based Multimedia Retrieval in the Presence of Unknown User Preferences

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  • Christian Beecks, Data Management and data Exploration Group, RWTH, Germany
  • Ira Assent
  • Thomas Seidl, Data Management and data Exploration Group, RWTH, Germany
  • Department of Computer Science
Content-based multimedia retrieval requires an appropriate similarity model which reflects user preferences. When these preferences are unknown or when the structure of the data collection is unclear, retrieving the most preferable objects the user has in mind is challenging, as the notion of similarity varies from data to data, from task to task, and ultimately from user to user. Based on a specific query object and unknown user preferences, retrieving the most similar objects according to some default similarity model does not necessarily include the most preferable ones. In this work, we address the problem of content-based multimedia retrieval in the presence of unknown user preferences. Our idea consists in performing content-based retrieval by considering all possibilities in a family of similarity models simultaneously. To this end, we propose a novel content-based retrieval approach which aims at retrieving all potentially preferable data objects with respect to any preference setting in order to meet individual user requirements as much as possible. We demonstrate that our approach improves the retrieval performance regarding unknown user preferences by more than 57% compared to the conventional retrieval approach.
Original languageEnglish
Book seriesLecture Notes in Computer Science
Pages (from-to)140-150
Number of pages9
Publication statusPublished - 2011
EventInternational Multimedia Modeling Conference - Taipei, Taiwan
Duration: 5 Jan 20117 Jan 2011
Conference number: 17


ConferenceInternational Multimedia Modeling Conference

Bibliographical note

Title of the vol.: Advances in Multimedia Modeling. Proceedings 17th International Conference on MultiMedia Modeling (MMM 2011), / Kuo-Tien Lee, Wen-Hsiang Tsai, Hong-Yuan Mark Liao, Tsuhan Chen, Jun-Wei Hsieh and Chien-Cheng Tseng (eds.)
ISBN: 978-3-642-17831-3

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