Automatic image semantic annotation based on image-keyword document model

Authors:
Xiangdong Zhou;Lian Chen;Jianye Ye;Qi Zhang;Baile Shi
Affiliations:
Department of Computing and Information Technology, Fudan University Shanghai, China;Department of Computing and Information Technology, Fudan University Shanghai, China;Department of Computing and Information Technology, Fudan University Shanghai, China;Department of Computer Science, University of North Carolina at Chapel Hill;Department of Computing and Information Technology, Fudan University Shanghai, China
Venue:
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Year:
2005

Citing 16
Cited 2

VisualSEEk: a fully automated content-based image query system

MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
A novel relevance feedback technique in image retrieval

MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Semantic Annotation of Sports Videos

IEEE MultiMedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Semantic Queries with Pictures: The VIMSYS Model

VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
A Bayesian Method for Content-Based Image Retrieval by Use of Relevance Feedback

VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Challenges of Image and Video Retrieval

CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Automatic image annotation and retrieval using cross-media relevance models

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Modeling annotated data

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Strategies for Positive and Negative Relevance Feedback in Image Retrieval

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
On image auto-annotation with latent space models

MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Automated annotation of human faces in family albums

MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Effective automatic image annotation via a coherent language model and active learning

Proceedings of the 12th annual ACM international conference on Multimedia
Hierarchical decision making scheme for sports video categorisation with temporal post-processing

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Content based annotation and retrieval in RAIDER

IRSG'98 Proceedings of the 20th Annual BCS-IRSG conference on Information Retrieval Research
Factor graph framework for semantic video indexing

IEEE Transactions on Circuits and Systems for Video Technology

Automatic image annotation by an iterative approach: incorporating keyword correlations and region matching

Proceedings of the 6th ACM international conference on Image and video retrieval
Some experiments of face annotation based on latent semantic indexing in FIARS

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel method of automatic image semantic annotation. Our approach is based on the Image-Keyword Document Model (IKDM) with image features discretization. According to IKDM, the image keyword annotation is conducted using image similarity measurement based on language model from text information retrieval domain. Through the experiments on a testing set of 5000 annotated images, our approach demonstrates great improvement of annotation performance compared with the known discretization-based image annotation model such as CMRM. Our approach also performs better in annotation time compared with the continuous model such as CRM.