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Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval

Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval. Advisor ： Dr. Hsu Presenter ： Chien Shing Chen Author: Wei-Hao Lin and Hsin-His Chen. Proceedings of 2003 Workshop of Cross Language Evaluation Forum, Norway, August, 2003. Outline. Motivation

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Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval

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  1. Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval Advisor ：Dr. Hsu Presenter： Chien Shing Chen Author: Wei-Hao Lin and Hsin-His Chen Proceedings of 2003 Workshop of Cross Language Evaluation Forum,Norway, August, 2003.

  2. Outline • Motivation • Objective • Introduction • Backward Transliteration • Query Translation • Experimental Result • Conclusions • Personal Opinion

  3. Motivation • How to retrieve multimedia data precisely a important research issue. • People with no strong language skills can easily understand the relevance of the retrieved images. • IR systems must handle proper nouns transliteration approximately to achieve better performance.

  4. Objective • adopt text-based approach to deal with the Chinese-English cross-language image retrieval problem

  5. Introduction Input MI 1 Chinese English F-2-H-F 2 IPA IPA Similarity score +MI Phoneme Phoneme 3 Similarity score +F2HF 4 F-2-H-F: First –two-highest-frequency MI: Mutual Information

  6. Similarity Measurement-Dynamic • Dynamic programming to trade off : • alignment • similarity scoring matrix M • OPTIMAL • S1 (j h u g oU) • S2 (v k uo)

  7. Candidate Filter • A transliterated word and its original word contain the same phonemes, and the order of the phonemes are the same. • After retrieving, the top rank of candidate words as the appropriate candidates of the transliterated word.

  8. Candidate Filter • x: Chinese phoneme • y: English phoneme

  9. Query Translation • We adopted the following two methods to select appropriate translations: • CO model • adopt MI to measure the co-occurrence strength between words • First-two-highest-frequency • highest occurrence frequency in the English image captions were considered as the target language query terms

  10. Query Translation • 150 distinct Chinese query terms • Total 16 of 150 query terms is unknown word. • The terms contain 7 person names and 5 location names, and were translated by foreign names.

  11. Experiments

  12. Experiments

  13. Experiments

  14. Conclusions • Text-based image retrieval and query translation were adopted in the experiments.

  15. Personal Opinion • Drawback • The corpus is not clear. • Application • apply to text-based IR • Future Work • identify unknown word is still a challenge

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