MSR-Bing Image Retrieval Challenge ,written by Win

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For 2014 MSR-Bing Image Retrieval Challenge
written by Win

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MSR-Bing Image Retrieval Challenge ,written by Win

  1. 1. Text Image Retrieval Challenge -Enhance relationships between query and image Instructor:MeiChenYeh ChenLinYu,ChiungWei Hsu VIPLAB
  2. 2. Outline 1. Proposed method 2. Evaluation Metric 3. Experiment Result 4. Finding and Difficulty 5. Demo 6. Conclusion 7. Future work
  3. 3. Proposed Method
  4. 4. Query Natural Language Processing Tokenization POSt QE by WordNet QE by Wikipedia WordNet Wikipedia Click_count ranking Top candidates User Clicklog from MSR dataset
  5. 5. Apple apple apples an apple ….
  6. 6. Query Processing 1. Stop word and removal 2. Tokenization 3. Stemming and Lemmatization 4. Part-of-speech Tagging 5. Wiki-suggestion (Misspelled words) 6. Expansion (wordnet and wikipeia)
  7. 7. Apple apple apples an apple …. Ranking Table log candidate count image apple 1890 QYQtQsx9lH 1KwA apple 503 QJ4gfSPJYh bw0A … … … apple mac 490 PvfGna70qGi BIA
  8. 8. Click-count Ranking MSR dataset provide real world data for user query log. With this, generated homemade searching table by“Click-count”. “Max click count rule” Log data 1,000,000 (only 1/20) We can make sure that candidate pictures are most popular.
  9. 9. Apple apple apples an apple …. Ranking Table log candidate count image apple 1890 QYQtQsx9lH 1KwA apple 503 QJ4gfSPJYh bw0A … … … apple mac 490 PvfGna70qGi BIA
  10. 10. Evaluation Metric
  11. 11. MSR vs DIY Method ! ! [rel]={Excellent=3,Good=2,Bad=0} X ✔
  12. 12. Experiment Result
  13. 13. Prepare and Work Off-line: NLTK to process user query log Build Ranking table (1,000,000) Include image(base64) to Database(800,000) On-line: NLTK to process query input Query expansion by word net and wikipedia Large-scale database query processing
  14. 14. Single unit-query 'president','frank','mars','chinese','taiwan',' dargon','crash','bird','France','Eiffel','presid ent','tony','frank','mars','chinese','taiwan','L ondon','Mexican','ydney', 'google','yahoo','jessica','microsoft','amazo n','windows','apple','line','linux','android', 'world','iphone','bacteria','cat','basketball',' dog','micky','tom','jerry','christmas','table', Test : 32 queries Acc:87.5 %
  15. 15. Compound word-query book store, picture frame, the lost and bewildered tourist, ice cream, cell phone, apple pie, a story as old as time, a cool wet afternoon, many cases of infectious disease swimming pool, the senlie old man,pencil box , long and winding road, tiddy bear , hot dog, jennifer love hewitt, some cookie shaped like stars hello kitty coloring page, kelly osbourne drinking, micky mouse, a wet amd stinky dog Test : 20 queries Acc:42.28 %
  16. 16. Finding and Difficulty
  17. 17. Spelling correctly can improve retrieval accuracy. Query expansion can find more related images ! A ambiguous query can be difficult to used. The gap exists between users and result images, because the word is polysemic. The user query still has a semantic problem. Finding
  18. 18. In a compound word query, the relationship between previous and next word is very important. Query semantic is still a challenge. Large-scale data processing is a big problem. How to speed up search performance? Difficulty
  19. 19. Demo
  20. 20. Conclusion
  21. 21. Enhance relationships between query and image
  22. 22. Find relationships between query and image
  23. 23. Future Work
  24. 24. Query Natural Language Processing Tokenization POSt QE by WordNet QE by Wikipedia WordNet Wikipedia Click_count ranking Top candidates Named Entity Recognition User Clicklog from MSR dataset Enhance
  25. 25. –ChenLin Yu, ChiungWei Hsu “Thank you”

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