1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Advisor ： Dr. Hsu Presenter ： Chien Shing Chen Author: Wei-Hao Lin and Hsin-His Chen Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval Proceedings of 2003 Workshop of Cross Language Evaluation Forum, Norway, August, 2003.

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Introduction Backward Transliteration Query Translation Experimental Result Conclusions Personal Opinion

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective adopt text-based approach to deal with the Chinese- English cross-language image retrieval problem

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Introduction Chinese Phoneme English IPA Similarity score +MI MI Phoneme IPA Input F-2-H-F F-2-H-F: First –two-highest-frequency MI: Mutual Information 1 2 3 4 Similarity score +F2HF

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Similarity Measurement-Dynamic Dynamic programming to trade off : alignment similarity scoring matrix M OPTIMAL S 1 (j h u g oU) S 2 (v k uo)

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Candidate Filter x: Chinese phoneme y: English phoneme

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusions Text-based image retrieval and query translation were adopted in the experiments.

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Personal Opinion Drawback The corpus is not clear. Application apply to text-based IR Future Work identify unknown word is still a challenge