Multi-label image annotation based on neighbor pair correlation chain

Authors:
Guang Jiang;Xi Liu;Zhongzhi Shi
Affiliations:
The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, Graduate University of Chinese Academy of Sciences, Beijin ...;Information Technology Laboratory, Fujitsu Research & Development Center Co., LTD., Beijing, China;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2012

Citing 10
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
Modeling annotated data

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Latent dirichlet allocation

The Journal of Machine Learning Research
Automatic multimedia cross-modal correlation discovery

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Manifold-ranking based image retrieval

Proceedings of the 12th annual ACM international conference on Multimedia
Image annotations by combining multiple evidence & wordNet

Proceedings of the 13th annual ACM international conference on Multimedia
Random k-Labelsets: An Ensemble Method for Multilabel Classification

ECML '07 Proceedings of the 18th European conference on Machine Learning
Classifier Chains for Multi-label Classification

ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Sorted label classifier chains for learning images with multi-label

Proceedings of the international conference on Multimedia
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Image annotation plays an important role in content-based image understanding, various machine learning methods have been proposed to solve this problem. In this paper, label correlation is considered as an undirected bipartite graph, in which each label are correlated by some common hidden topics. As a result, given a label, random walk with restart on the graph supplies a most related label, repeating this precedure leads to a label chain, which keep each adjacent labels pair correlated as maximally as possible. We coordinate the labels chain with its respective classifier training on bottom feature, and guide a classifier chain to annotate an image. The experiment illustrates that our method outperform both the baseline and another popular method.