tag ranking
DESCRIPTION
Tag Ranking. Present by Jie Xiao. Dept. of Computer Science Univ. of Texas at San Antonio. Outline. Problem Probabilistic tag relevance estimation Random walk tag relevance refinement Experiment Conclusion. Problem. - PowerPoint PPT PresentationTRANSCRIPT
Tag Ranking
Present by Jie Xiao
Dept. of Computer Science
Univ. of Texas at San Antonio
Outline
ProblemProbabilistic tag relevance estimationRandom walk tag relevance refinementExperimentConclusion
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.
Problem (Cont.)
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.
Probabilistic Tag Relevance Estimation
Similarity between a tag and an image
x : an imaget : tag i associated with image xP(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
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
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 densityHistogramKernel density estimator
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 tixk : the top k near neighbor image in image set XiK : density kernel function used to estimate the probability|x| : cardinality of Xi
Relevance between tags
ti, tag i associated with image xtj, tag j associated with image x , the image set containing tag i , the image set containing tag jN: the top N nearest neighbor for image x
Relevance between tags (Cont.)
Relevance between tags (Cont.)
Co-occurrence similarity between tags
f(ti) : the # of images containing tag tif(ti,tj) : the # of images containing both tag ti and tag tjG : the total # of images in Flickr
Relevance between tags (Cont.)
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
Random walk
Random walk over tag graph (Cont.)
Experiments
Dataset: 50,000 image crawled from FlickrPopular tags:Raw tags: more than 100,000 unique tagsFiltered tags: 13,330 unique tags
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 optimalranking’s NDCG score is 1.
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.
Thank you!