Image annotations based on semi-supervised clustering with semantic soft constraints

Authors:
Rui Xiaoguang;Yuan Pingbo;Yu Nenghai
Affiliations:
MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei, Anhui, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei, Anhui, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei, Anhui, China
Venue:
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Year:
2006

Citing 8
Cited 0

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
Constrained K-means Clustering with Background Knowledge

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Clustering with Instance-level Constraints

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Using syntactic dependency as local context to resolve word sense ambiguity

ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A probabilistic framework for semi-supervised clustering

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Novel Approach to Auto Image Annotation Based on Pairwise Constrained Clustering and Semi-Naïve Bayesian Model

MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Using information content to evaluate semantic similarity in a taxonomy

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

An efficient image annotation and retrieval system is highly desired for the increase of amounts of image information. Clustering algorithms make it possible to represent images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words, have been published for image annotation. But most of these models cluster only using visual features, ignoring semantics of images. In this paper, we propose a novel model based on semi-supervised clustering with semantic soft constraints which can utilize both visual features and semantic meanings. Our method first measures the semantic distance with generic knowledge (e.g. WordNet) between regions of the training images with manual annotations. Then a semi-supervised clustering algorithm with semantic soft constraints is proposed to cluster regions with semantic soft constraints which are formed by semantic distance. The experiment results show that our model improves performance of image annotation and retrieval system.