Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2007

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.

Keywords

Apriori, Image annotation, Multiple-instance learning

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Information Systems and Management

Publication

Multimedia Modeling: 13th International Conference, MMM 2007, Singapore, January 9-12: Proceedings

Volume

4351

First Page

269

Last Page

278

ISBN

9783540694212

Identifier

10.1007/978-3-540-69423-6_27

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-540-69423-6_27

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