Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Backward machine transliteration by learning phonetic similarity
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Learning formulation and transformation rules for multilingual named entities
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From text to image: generating visual query for image retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
A corpus-based relevance feedback approach to cross-language image retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Language translation and media transformation in cross-language image retrieval
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This paper explores the integration of textual and visual information for cross-language image retrieval. An approach which automatically transforms textual queries into visual representations is proposed. The relationships between text and images are mined. We employ the mined relationships to construct visual queries from textual ones. The retrieval results of textual and visual queries are combined. We conduct English monolingual and Chinese-English cross-language retrieval experiments to evaluate the proposed approach. The selection of suitable textual query terms to construct visual queries is the major concern. Experimental results show that the proposed approach improves retrieval performance, and nouns are appropriate to generate visual queries.