Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2010

Abstract

Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources, semantic context has been exploited in AIA and brings promising results. However, previous works either casted the problem into structural classification or adopted multi-layer modeling, which suffer from the problems of scalability or model efficiency. In this paper, we propose a novel discriminative Conditional Random Field (CRF) model for semantic context modeling in AIA, which is built over semantic concepts and treats an image as a whole observation without segmentation. Our model captures the interactions between semantic concepts from both semantic level and visual level in an integrated manner. Specifically, we employ graph structure to model contextual relationships between semantic concepts. The potential functions are designed based on linear discriminative models, which enables us to propose a novel decoupled hinge loss function for maximal margin parameter estimation. We train the model by solving a set of independent quadratic programming problems with our derived contextual kernel. The experiments are conducted on commonly used benchmarks: Corel and TRECVID data sets for evaluation. The experimental results show that compared with the state-of-the-art methods, our method achieves significant improvement on annotation performance.

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, June 13-18

First Page

3368

Last Page

3375

ISBN

9781424469840

Identifier

10.1109/CVPR.2010.5540015

Publisher

IEEE

City or Country

San Francisco

Download

Share

COinS