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How ‘How’ Reflects What’s What: Content-based Exploitation of How Users Frame Social Images

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Our presentation at the High Risk / High Reward track at the ACM MM 2014 conference. In this presentation we present a novel way to tackle large scale image classification or retrieval.

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How ‘How’ Reflects What’s What: Content-based Exploitation of How Users Frame Social Images

  1. 1. How ‘How’ Reflects What’s What: Content-based Exploitation of How Users Frame Social Images Michael Riegler, Simula Research Laboratory, Norway! Martha Larson, Delft University of Technology, Netherlands! Mathias Lux, University of Klagenfurt, Austria! Christoph Kofler, Delft University of Technology, Netherlands
  2. 2. We will introduce a signal that ! exists in every image collection & gives you an enormous speedup!
  3. 3. Take Home Message ❖ Photographers use intentional frames.! ❖ The frames reflect the semantic categories of images.! ❖ In turn, global image features reflect the frames.! ❖ This motivates a fast and simple approach to image semantics.! ❖ Take home a strong inner feeling that you want to try it out yourself!
  4. 4. But what is intentional framing?
  5. 5. ❖ You may think now that you already know it, its called:! ❖ Concepts or…! ❖ Scenes! ❖ But Wrong!
  6. 6. ❖ And let me tell you, it is also not! ❖ Composition! ❖ Also Wrong!
  7. 7. “Intentional framing is the sum of choices made by photographers on exactly how to portray the subject matter that they have decided to photograph.” –The Definition Picture source: https://www.flickr.com/photos/ausnap/5712791522/in/photostream/
  8. 8. Mechanics of Intentional Framing semantic reflects reflects category of an image the photographers´ intent global image features reflects
  9. 9. Time for examples…
  10. 10. Hypothesis ❖ Photographers’ choices.! ❖ Even if framing is not a conscious decision, it still is an unconscious one.! ❖ Similar intents for taking images lead to similar framings.! ❖ Global features can capture these intentional semantics.
  11. 11. The Exploration Experiments…
  12. 12. Global Features and Intent ❖ Global features connect semantics and intent.! ❖ Show that there exist a solid evidence for intentional framing.! ❖ Clustering experiment on two different data sets! ❖ Intent data set! ❖ Fashion 10000 data set
  13. 13. Correlation of Peoples’ Perception and Global Features ❖ X-means clustering! ❖ Based on different global features.! ❖ Features can catch different aspects (edges, colour, etc.).! ❖ The density of the global features based clusters correlated to the users perception about the intentional framing in it. Original Edge Color
  14. 14. Evidence of Human Perception of Intent black - a positive correlation! red - a negative correlation Intent Categories ! Global Features 1 2 3 4 5 6 CEDD FCTH Gabor Tamura Luminance Layout Scalable Color Opponent Histogram Autocolor Correlogram JPEG Coefficent Edge Histogram PHOG JCD Joint Histogram
  15. 15. Correlation between semantic categories and global features correlation of 0,56
  16. 16. The Application Experiments…
  17. 17. Content Based Classification ❖ Using intentional framing to tackle a classification problem.! ❖ Simple search-based classifier (SimSea).! ❖ Our submission to the ACM MM `13 Yahoo! - Large-scale Flickr-tag image Classification Grand Challenge! ❖ Reviewers told us: It is too simple…
  18. 18. Remember the challenge? ❖ 2 million images.! ❖ 10 different semantic categories.! ❖ nature, people, music, london, 2012, food, wedding, sky, beach, travel.! ❖ extremely diverse categories.
  19. 19. JCD CL OH PHOG 2012 0,198 0,128 0,130 0,104 beach 0,448 0,487 0,342 0,534 food 0,531 0,492 0,389 0,352 london 0,244 0,201 0,146 0,347 music 0,526 0,457 0,495 0,164 nature 0,502 0,410 0,435 0,503 people 0,264 0,227 0,244 0,105 sky 0,628 0,601 0,544 0,473 travel 0,139 0,101 0,128 0,112 wedding 0,463 0,272 0,262 0,235 The results iAP per category based on the development set
  20. 20. Compared to the Official Results ! ! Our method! SimSea Local 1 (SMaL[1]) Local 2 (SVM[1]) ❖ Very good results with a very simple method.! ❖ Very time efficient.! ❖ Processed on a single desktop PC. Concept 1 (HA[2]) MiAP 0,391 0,422 0,413 0,37 [1] E. Mantziou, S. Papadopoulos, and Y. Kompatsiaris. Scalable Training with Approximate Incremental Laplacian Eigenmaps and PCA. In Proceedings of the ACM MM 13’, pages 381–384, 2013. [2] W. Hsu. Flickr-tag Prediction Using Multi-modal Fusion and Meta Information. In Proceedings of ACM MM 13’, pages 353– 356, 2013.
  21. 21. Conclusion ❖ Intentional framing exists.! ❖ Different framing correspond to different global features.! ❖ Interesting framework for leveraging global features classification.! ❖ Fast and simple!! ❖ New vista for multimedia research.
  22. 22. Questions? Thank you!

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