Mining multiple visual appearances of semantics for image annotation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationAdvances in Multimedia Modeling
Subtitle of host publication13th International Multimedia Modeling Conference, MMM 2007, Proceedings
PublisherSpringer Verlag
Pages269-278
Volume4351 LNCS
ISBN (Print)9783540694212
Publication statusPublished - 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4351 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title13th International Multimedia Modeling Conference, MMM 2007
PlaceSingapore
CitySingapore
Period9 - 12 January 2007

Abstract

This paper investigates the problem of learning the visual semantics of keyword categories for automatic image annotation. Supervised learning algorithms which learn only a single concept point of a category are limited in their effectiveness for image annotation. We propose to use data mining techniques to mine multiple concepts, where each concept may consist of one or more visual parts, to capture the diverse visual appearances of a single keyword category. For training, we use the Apriori principle to efficiently mine a set of frequent blobsets to capture the semantics of a rich and diverse visual category. Each concept is ranked based on a discriminative or diverse density measure. For testing, we propose a level-sensitive matching to rank words given an unannotated image. Our approach is effective, scales better during training and testing, and is efficient in terms of learning and annotation. © Springer-Verlag Berlin Heidelberg 2007.

Research Area(s)

  • Apriori, Image annotation, Multiple-instance learning

Citation Format(s)

Mining multiple visual appearances of semantics for image annotation. / Tan, Hung-Khoon; Ngo, Chong-Wah.

Advances in Multimedia Modeling: 13th International Multimedia Modeling Conference, MMM 2007, Proceedings. Vol. 4351 LNCS Springer Verlag, 2007. p. 269-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4351 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review