Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2009

Abstract

Much research effort on Automatic Image Annotation (AIA) has been focused on Generative Model, due to its well formed theory and competitive performance as compared with many well designed and sophisticated methods. However, when considering semantic context for annotation, the model suffers from the weak learning ability. This is mainly due to the lack of parameter setting and appropriate learning strategy for characterizing the semantic context in the traditional generative model. In this paper, we present a new approach based on Multiple Markov Random Fields (MRF) for semantic context modeling and learning. Differing from previous MRF related AIA approach, we explore the optimal parameter estimation and model inference systematically to leverage the learning power of traditional generative model. Specifically, we propose new potential function for site modeling based on generative model and build local graphs for each annotation keyword. The parameter estimation and model inference is performed in local optimal sense. We conduct experiments on commonly used benchmarks. On Corel 5000 images [3], we achieved 0.36 and 0.31 in recall and precision respectively on 263 keywords. This is a very significant improvement over the best reported result of the current state-of-the-art approaches.

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, June 20-25

First Page

1153

Last Page

1160

ISBN

9781424439935

Identifier

10.1109/CVPRW.2009.5206518

Publisher

IEEE Computer Society

City or Country

Miami

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