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
Two languages are more informative than one
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Scalable search-based image annotation of personal images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Bipartite graph reinforcement model for web image annotation
Proceedings of the 15th international conference on Multimedia
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Automatic annotation of digital pictures is a key technology for managing and retrieving images from large image collections. Typical algorithms only deal with the problem of monolingual image annotation. In this paper, we propose a framework to deal with the problem of multilingual image annotation, which can annotate images in multiple languages. The framework can not only benefit users with different native languages, but also provide more accurate annotations. In this framework, image annotation is performed in two stages, including parallel monolingual image annotation and the fusion of annotation results in multiple languages. In the first stage, candidate annotations for each language are extracted by leveraging multilingual large scale web image database. Due to the incompleteness and inaccuracy problem of candidate annotations, we proposed a multilingual annotation fusion algorithm (MAF). By modeling candidate annotations for each language as an n-partite graph, MAF algorithm can improve and re-rank multilingual annotations. Finally, annotations with the highest ranking values in each language are selected and translated as the result. Experimental results for English-Chinese image annotations demonstrate the effectiveness of the proposed framework.