Callisto: Content Based Tag Recommendation for Images
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  • 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…