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An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

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01 de jun de 2015
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Sharing Your Destination with the World: Lessons Learned Using Flickr
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An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

01 de jun de 2015
• 76 recomendaciones • 5.314 visualizaciones

Descargar para leer sin conexión

Arte y fotografía Diseño Tecnología

We propose to use a computer vision method to surface aesthetically pleasant pictures from the immense pool of near-zero-popularity items.
[@ICWSM 2015, Oxford]

[Transcript]
Have a look at these two Flickr pictures. What’s the difference between them? They are both aesthetically pleasant photos, however their social exposure is very different. Picture A received a lot of attention as showed by the high number of favorites, comments, and views while picture B has very low social signals. These are examples of respectively popular and unpopular content.

Considering the 100M creative commons Flickr dataset, we found that only 2% of the pictures have more than 5 favorites while the remaining 98% have five or less. The vast majority of the content lies below the surface of attention. While exploratory interfaces like the Flickr Explorer page tend to promote appealing and popular images, what happens to the unpopular content? Is it possible that all those 98M pictures do not deserve attention?

In our work instead of focusing on the aspects that make an item popular we flip the perspective and we take a look at the quality of the unpopular content trying to surface the hidden gems of Flickr pictures. To infer image aesthetic quality we resort to crowdsourcing.

We sampled 10K images within 4 topical categories (nature, urban, people and animals). Because we want to see how quality varies with popularity within each category we randomly sample popular, average popular and unpopular pictures. We design a crowdsourcing task where a user has to evaluate how beautiful is an image on a 5-grade scale. We ask at least 5 independent judgments per image.

Even if there is a positive correlation between popularity and beauty (that means on average popular pictures are perceived as appealing) there are popular photos that have low aesthetic value. This is not surprising since popularity is not driven only by intrinsic quality as shown in the past. More importantly, several pictures with near-zero favorites have been judged as very appealing. Their relative amount is very low, making any random sampling strategy totally ineffective.

Social signals like comments or tags are most of the time ineffective since unpopular items rarely receive social feedback that means you cannot use tags or comments to surface them. A possible solution is to look at the pixels. To surface the high quality content we use the crowdsourced dataset to learn a computational aesthetic framework based on compositional visual features. Such framework is able to automatically score images according to their aesthetic value.

With this algorithm we ranked 9M unpopular Flickr photos. We then went back to the crowd and we asked the workers to judge the top 200 rated photos, the most beautiful according to our algorithm. Consistently across categories, the perceived beauty of the surfaced images is comparable to the most popular photos.

We propose to use a computer vision method to surface aesthetically pleasant pictures from the immense pool of near-zero-popularity items.
[@ICWSM 2015, Oxford]

[Transcript]
Have a look at these two Flickr pictures. What’s the difference between them? They are both aesthetically pleasant photos, however their social exposure is very different. Picture A received a lot of attention as showed by the high number of favorites, comments, and views while picture B has very low social signals. These are examples of respectively popular and unpopular content.

Considering the 100M creative commons Flickr dataset, we found that only 2% of the pictures have more than 5 favorites while the remaining 98% have five or less. The vast majority of the content lies below the surface of attention. While exploratory interfaces like the Flickr Explorer page tend to promote appealing and popular images, what happens to the unpopular content? Is it possible that all those 98M pictures do not deserve attention?

In our work instead of focusing on the aspects that make an item popular we flip the perspective and we take a look at the quality of the unpopular content trying to surface the hidden gems of Flickr pictures. To infer image aesthetic quality we resort to crowdsourcing.

We sampled 10K images within 4 topical categories (nature, urban, people and animals). Because we want to see how quality varies with popularity within each category we randomly sample popular, average popular and unpopular pictures. We design a crowdsourcing task where a user has to evaluate how beautiful is an image on a 5-grade scale. We ask at least 5 independent judgments per image.

Even if there is a positive correlation between popularity and beauty (that means on average popular pictures are perceived as appealing) there are popular photos that have low aesthetic value. This is not surprising since popularity is not driven only by intrinsic quality as shown in the past. More importantly, several pictures with near-zero favorites have been judged as very appealing. Their relative amount is very low, making any random sampling strategy totally ineffective.

Social signals like comments or tags are most of the time ineffective since unpopular items rarely receive social feedback that means you cannot use tags or comments to surface them. A possible solution is to look at the pixels. To surface the high quality content we use the crowdsourced dataset to learn a computational aesthetic framework based on compositional visual features. Such framework is able to automatically score images according to their aesthetic value.

With this algorithm we ranked 9M unpopular Flickr photos. We then went back to the crowd and we asked the workers to judge the top 200 rated photos, the most beautiful according to our algorithm. Consistently across categories, the perceived beauty of the surfaced images is comparable to the most popular photos.

Arte y fotografía Diseño Tecnología
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An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

  1. 1. An  Image  is  Worth  More  than  a   Thousand  Favorites   Surfacing  the  Hidden  Beauty  of  Flickr  Pictures  
  2. 2. Rossano  Schifanella   Miriam  Redi   Luca  Maria  Aiello   @rschifan     @mad_astronaut     @lajello  
  3. 3. A   B  
  4. 4. A   B   favorites   comments   views   686   45   18,475   Popular   Unpopular   favorites   comments   views   3   1   190  
  5. 5. 98%   2%   ≥  6     ≤5   Flickr  100M  
  6. 6. 99+   favorites  
  7. 7. Flickr  100M  
  8. 8. 1  EXPLORE  UNPOPULAR  CONTENT   2  SURFACE  HIDDEN  GEMS  
  9. 9. Crowdsourcing  image  aesthe]cs   judgments    
  10. 10. 10K  photos   Full  popularity  range  
  11. 11. Evaluate  the  beauty  of  the  picture,  NOT   the  beauty  of  the  subject   Instruc]ons   Examples  for  each  score  
  12. 12. Good  inter-­‐rater  agreement  
  13. 13. Large  ground  truth  of  aesthe]c   scores  made  by  non-­‐expert     di.unito.it/beautyicwsm15   10K   photos   60K scores  
  14. 14. popularity  ~  beauty  (0.43)  
  15. 15. popular  photos  can  have   low  aesthe]c  value  
  16. 16. Full  range  of  beauty  judgments  
  17. 17. Flickr  100M   beau]ful  hidden  photos    
  18. 18. 0 5 10 15 20 25 30 0 5 10 15 20 25 30 average # favorites Comments Pool Tags unpopular  items  rarely  receive  social   feedback  
  19. 19. PARTIAL  LEAST  SQUARES   REGRESSION   CrowdBeauty   CROWDSOURCED   DATASET   COMPOSITIONAL  VISUAL   FEATURES   Photographic   Rules  
  20. 20. 9M  Flickr  CC  pictures   200  top  CrowdBeauty    vs  baselines   Evalua]on  
  21. 21. 1   1.5   2   2.5   3   3.5   4   people   urban   nature   animals   Unpopular         Popular     CrowdBeauty       CrowdBeauty  Evalua]on   Averagebeautyscore
  22. 22. 1   1.5   2   2.5   3   3.5   4   people   urban   nature   animals   CrowdBeauty       CVBaseline   CrowdBeauty  Evalua]on   Averagebeautyscore

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