1. Callisto – A Content-based Tag
Recommendation Tool
M. Lux, A. Pitman, and O. Marques

2. What does Callisto do?
• Given an image and one or more start tags
• Callisto finds ranked tag recommendations
1. Based on our model (NCP)
2. Based on statistical analysis (Stat)

3. What are the benefits of NCP & Callisto?
• Different tags are suggested.
• Tags are re-ranked based on visual content.
– Consequently:
• With the NCP model, it is common to see tags that are
highly related to visual features being suggested if such
features are there, and not suggested if those features
are missing.
– E.g.: sunset is not suggested if typical colors of sunsets are
missing in the image.

4. The Application
Image to be tagged
Start tag(s)
Low-level
features used
Suggestions

5. Ranked suggested tags
Suggestions by our content-based model (NCP)
Suggestions based on tag co-occurrence (baseline)

6. Use Case: Beach
Start tag: beach
• Suggestions by both models
are almost the same
• Both feature good quality
suggestions

7. Use Case: Beach
Same start tag, different photo
• Suggestions differ
• NCP has different tags to offer

8. Use Case: Ocean
Start tag: ocean
• NCP suggests clouds

9. Use Case: Juggling
Start tag: juggling
• NCP ranks fire first
• NCP doesn‘t include balls in
the list, which is good, since
there are no balls involved

10. Use Case: Juggling
Start tag: juggling
• NCP suggests portrait
and people
• NCP doesn‘t suggest fire

11. Use Case: Juggling girl
Start tags: juggling girl
• NCP suggests woman
• NCP ranks people higher

12. Performance issues
• Callisto has to download images and tags for
suggestions, which is slow.
• Callisto caches downloads, so next time (with
the same start tag) it is much faster.
• The number of downloaded photos is critical.
– 28 works fine and is not too slow
– 100 is much better, but downloading takes forever

13. Live demo
• Keep your fingers crossed…