Presentation on theme: "Dong Liu Xian-Sheng Hua Linjun Yang Meng Weng Hong-Jian Zhang."— Presentation transcript:
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*.
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. 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.
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.
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.