Modeling Personal and Social Network Context for Event Annotation in Images

Date

2007

Authors

Shevade, Bageshree
Sundaram, Hari
Xie, Lexing

Journal Title

Journal ISSN

Volume Title

Publisher

Association for Computing Machinery Inc (ACM)

Abstract

This paper describes a framework to annotate images using personal and social network contexts. The problem is important as the correct context reduces the number of image annotation choices.. Social network context is useful as real-world activities of members of the social network are often correlated within a specific context. The correlation can serve as a powerful resource to effectively increase the ground truth available for annotation. There are three main contributions of this paper: (a) development of an event context framework and definition of quantitative measures for contextual correlations based on concept similarity in each facet of event context; (b) recommendation algorithms based on spreading activations that exploit personal context as well as social network context; (c) experiments on real-world, everyday images that verified both the existence of inter-user semantic disagreement and the improvement in annotation when incorporating both the user and social network context. We have conducted two user studies, and our quantitative and qualitative results indicate that context (both personal and social) facilitates effective image annotation.

Description

Keywords

Keywords: Content management; Event annotation; Real world activities; Social networks; Algorithms; Correlation methods; Problem solving; Semantics; User interfaces; Image analysis Content management; Context; Event annotation; Images; Multimedia; Social networks

Citation

Source

Proceedings of ACM/IEEE Joint Conference on Digital Libraries, JCDL 2007

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

10.1145/1255175.1255200

Restricted until

2037-12-31