Tag Ranking

1. Tag Ranking Present by Jie Xiao Dept. of Computer Science Univ. of Texas at San Antonio

2. Outline • Problem • Probabilistic tag relevance estimation • Random walk tag relevance refinement • Experiment • Conclusion jxiao@cs.utsa.edu

3. Problem • There are millions of social images on internet, which are very attractive for the research purpose. • The tags associated with images are not ordered by the relevance. jxiao@cs.utsa.edu

4. Problem (Cont.) jxiao@cs.utsa.edu

5. Tag relevance • There are two types of relevance to be considered. • The relevance between a tag and an image • The relevance between two tags for the same image. jxiao@cs.utsa.edu

6. Probabilistic Tag Relevance Estimation • Similarity between a tag and an image x : an image t : tag i associated with image x P(t|x) : the probability that given an image x, we have the tag t. P(t) : the prior probability of tag t occurred in the dataset After applying Bayes’ rule, we can derive that jxiao@cs.utsa.edu

7. Probabilistic Relevance Estimation (Cont) • Since the target is to rank that tags for the individual image and p(x) is identical for these tags, we refine it as jxiao@cs.utsa.edu

8. Density Estimation • Let (x1, x2, …, xn) be an iid sample drawn from some distribution with an unknown density ƒ. • Two types of methods to describe the density • Histogram • Kernel density estimator jxiao@cs.utsa.edu

9. Histogram Credit: All of Nonparametric Statistics via UTSA library jxiao@cs.utsa.edu

10. Kernel Density Estimation Smooth function K is used to estimate the density jxiao@cs.utsa.edu

11. Kernel Density Estimation (Cont.) • Its kernel density estimator is jxiao@cs.utsa.edu

12. Probabilistic Relevance Estimation (Cont) • Kernel Density Estimation (KDE) is adopted to estimate the probability density function p(x|t). Xi : the image set containing tag ti xk : the top k near neighbor image in image set Xi K : density kernel function used to estimate the probability |x| : cardinality of Xi jxiao@cs.utsa.edu

13. Relevance between tags • ti, tag i associated with image x • tj, tag j associated with image x • , the image set containing tag i • , the image set containing tag j • N: the top N nearest neighbor for image x jxiao@cs.utsa.edu

14. Relevance between tags (Cont.) jxiao@cs.utsa.edu

15. Relevance between tags (Cont.) • Co-occurrence similarity between tags f(ti) : the # of images containing tag ti f(ti,tj) : the # of images containing both tag ti and tag tj G : the total # of images in Flickr jxiao@cs.utsa.edu

16. Relevance between tags (Cont.) jxiao@cs.utsa.edu

17. Relevance between tags (Cont.) • Relevance score between two tags where jxiao@cs.utsa.edu

18. Random walk over tag graph P: n by n transition matrix. pij : the probability of the transition from node i to j rk(j): relevance score of node i at iteration k jxiao@cs.utsa.edu

19. Random walk jxiao@cs.utsa.edu

20. Random walk over tag graph (Cont.) jxiao@cs.utsa.edu

21. Experiments • Dataset: 50,000 image crawled from Flickr • Popular tags: • Raw tags: more than 100,000 unique tags • Filtered tags: 13,330 unique tags jxiao@cs.utsa.edu

22. Performance Metric • Normalized Discounted Cumulative Gain (NDCG) r(i) : the relevance level of the i - th tag Zn : a normalization constant that is chosen so that the optimal ranking’s NDCG score is 1. jxiao@cs.utsa.edu

23. Experimental Result • Comparison among different tag ranking approaches jxiao@cs.utsa.edu

24. jxiao@cs.utsa.edu

25. Conclusion • Estimate the tag - image relevance by kernel density estimation. • Estimate the tag – tag relevance by visual similarity and tag co-occurrence. • A random walk based approach is used to refine the ranking performance. jxiao@cs.utsa.edu

26. Thank you! jxiao@cs.utsa.edu