Dong Liu
Xian-Sheng Hua
Linjun Yang
Meng Weng
Hong-Jian Zhang

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.
Plan

Introduction

Tag Ranking Scheme

Performance Evaluation

Applications

Conclusion
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.


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*.

Introduction

Tag Ranking Scheme

Performance Evaluation

Applications

Conclusion
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.
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.
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.
Tag exemplar similarity

Tag exemplar similarity: For tag t associated with
image x, collect N nearest neigbours[exemplars]
from images containing tag t.
Concurrence similarity

Based on how often tags co-occur in a
list.

Combine the two similarities and then
apply random walk.
1.
2.
3.

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.

Introduction

Tag Ranking Scheme

Performance Evaluation

Applications

Conclusion
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


Baseline : Original

PTR: Probabilistic
tag ranking(Step 1)

RWTR: Random
Walk TR (Step 2)

Combination of
Step1 and Step2

Introduction

Tag Ranking Scheme

Performance Evaluation

Applications

Conclusion
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. 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.

Introduction

Tag Ranking Scheme

Performance Evaluation

Applications

Conclusion
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.
Thank you !!