Tag Ranking (Flickr)

1. Dong Liu  Xian-ShengHua LinjunYang MengWeng Hong-Jian Zhang Tag Ranking (Flickr)

2. Social media sharing web sites allow users to annotate images with free tags. e.g. : Flickr • Tags are not in any specific order; not based on relevance or important information. • Limits effectiveness of tags in search and other applications. • Scheme to automatically rank the tags based on relevance/importance.

3. Plan • Introduction • Tag Ranking Scheme • Performance Evaluation • Applications • Conclusion

4. Intro • Flickr: Social media sharing website. Tagging makes Flickr photos better accessible to the public. • Existing studies show that only 50 % of tags are actually associated to the image. •  Importance of tags cannot be distinguished from current tag list; order is just according to input sequence and carries little information about the importance.

5. Lack of this information in the tag list has significantly limited the application of tags. • In Flickr tag based image search , currently it does not give an option of sorting tagged images based on importance/relevance. • Currently, you can sort out images based on 'recentness' or 'interestingness.‘ • * First study addressing this issue*.

6. Introduction • Tag Ranking Scheme • Performance Evaluation • Applications • Conclusion

7. Tag Ranking Scheme • Step 1: Probabilistic method to estimate the initial relevance score of each tag for one image individually. • Step 2: Implement a random-walk based process to mine the association/correlation between tags.

8. Step 1: The Probabilistic method • Given a tag t, its relevance score to an image x is defined as s(t, x) = p(t/x)/p(t) • Straightforwardly, p(t/x) can be said to be the score. • Problem: the tag might appear too frequently and hence, p(t/x) will be 1. The tag is non-informative. • Solution: Normalize p(t/x) by p(t) to penalize frequently appearing tags.

9. Step 2: Random Walk-based Refinement • Step 1 doesn’t take into account association between tags. E.g.: “cat”, “kitten”, “animal” and “Nikon” • Tag Graph : Nodes of the graph are tags of the image and the edges are weighted with pair wise tag similarity.

10. Tag exemplar similarity • Tag exemplar similarity: For tag t associated with image x, collect N nearest neigbours[exemplars] from images containing tag t.

11. Concurrence similarity • Based on how often tags co-occur in a list.

12. Combine the two similarities and then apply random walk. • Vj=initial probabilistic relevance score of tag tj • α=weight parameter that belongs to (0,1) • pij=indicates probability of transition from node i to node j • This step will promote tags that have close neighbors and weaken isolated tags.

13. Introduction • Tag Ranking Scheme • Performance Evaluation • Applications • Conclusion

14. Performance Evaluation • Dataset comprising 50k images from Flickr. • Perform tag based search: ‘interestingness’ • Top 5k images; collect tags. • After eliminating noise: 13,330 unique tags. • Evaluation measure: NDCG

15. Baseline : Original • PTR: Probabilistic tag ranking(Step 1) • RWTR: Random Walk TR (Step 2) • Combination of Step1 and Step2

16. Introduction • Tag Ranking Scheme • Performance Evaluation • Applications • Conclusion

17. Applications • Tag based image search: Based on importance/relevance. • Tag recommendation : For a given image, we provide the most important tags of its neighbors as recommendation. SelectK nearest neighbors. Collect top m tags of each neighbor and recommend them. • Image Group recommendation : Given an image, we use top tags in the ranked tag list to search for possible groups for sharing.

18. Introduction • Tag Ranking Scheme • Performance Evaluation • Applications • Conclusion

19. Conclusion • Tags associated with Flickr images are without specific order. • Limits effectiveness of tags. • Experimental results have shown that this scheme can order tags based on importance and that it is quite effective.

20. Thank you !!