1. Dong Liu Xian-Sheng Hua Linjun Yang Meng Weng Hong-Jian Zhang

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. 1. V j =initial probabilistic relevance score of tag t j 2. α=weight parameter that belongs to (0,1) 3. p ij =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  T ag based image search: Based on importance/relevance.  Tag recommendation : For a given image, we provide the most important tags of its neighbors as recommendation. Select K 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 !!