1. Collections for Automatic Image Annotation and Photo Tag Recommendation Philip McParlane, Yashar Moshfeghi and Joemon M. Jose University of Glasgow, UK http://www.dcs.gla.ac.uk/~philip/p.mcparlane.1@research.gla.ac.uk

2. Motivation for annotating images Problems with existing automatic image annotation collections Problems with existing photo tag recommendation collections Flickr-AIA Flickr-PTR Conclusions We introduce

3. Motivation for annotating images Problems with existing automatic image annotation collections Problems with existing photo tag recommendation collections Flickr-AIA Flickr-PTR Conclusions We introduce

4. With the amount of multimedia data rapidly increasing, it becomes important to organize this content effectively Social image sharing websites depend on manual annotation of their images. This has a large human cost however. Plus, humans often tag with irrelevant tags (e.g. girl) or tags which are opinionated (e.g. cool) etc.

5. With the amount of multimedia data rapidly increasing, it becomes important to organize this content effectively Social image sharing websites depend on manual annotation of their images. This has a large human cost however. Plus, humans often tag with irrelevant tags (e.g. girl) or tags which are opinionated (e.g. cool) etc.

6. With the amount of multimedia data rapidly increasing, it becomes important to organize this content effectively Social image sharing websites depend on manual annotation of their images. This has a large human cost however. Plus, humans often tag with irrelevant tags (e.g. girl) or tags which are opinionated (e.g. cool) etc.

7. Therefore, research has focussed on the automatic annotation of images Automatic image annotation (AIA) Photo Tag Recommendation (PTR) AIA Collections Many public collections used PTR Collections Mostly non-public collections used Evaluated on Corel5k, Corel30k, ESP Game, IAPR, Google Images, LabelMe, Washington Collection, Caltech, TrecVid 2007, Pascal 2007, MiAlbum & 4 other small collections. The 20 most cited AIA papers on CiteSeerX revealed that at least 15 collections had been used… For photo tag recommendation, the most popular works use their own collections. considers the pixels considers those tags already added Evaluated on

8. Therefore, research has focussed on the automatic annotation of images Automatic image annotation (AIA) Photo Tag Recommendation (PTR) AIA Collections Many public collections used PTR Collections Mostly non-public collections used Evaluated on Corel5k, Corel30k, ESP Game, IAPR, Google Images, LabelMe, Washington Collection, Caltech, TrecVid 2007, Pascal 2007, MiAlbum & 4 other small collections. The 20 most cited AIA papers on CiteSeerX revealed that at least 15 collections had been used… For photo tag recommendation, the most popular works use their own collections. considers the pixels considers those tags already added Evaluated on

9. Therefore, research has focussed on the automatic annotation of images Automatic image annotation (AIA) Photo Tag Recommendation (PTR) AIA Collections Many public collections used PTR Collections Mostly non-public collections used Evaluated on Corel5k, Corel30k, ESP Game, IAPR, Google Images, LabelMe, Washington Collection, Caltech, TrecVid 2007, Pascal 2007, MiAlbum & 4 other small collections. The 20 most cited AIA papers on CiteSeerX revealed that at least 15 collections had been used… For photo tag recommendation, the most popular works use their own collections. considers the pixels considers those tags already added Evaluated on

10. Motivation for annotating images Problems with existing automatic image annotation collections Problems with existing photo tag recommendation collections Flickr-AIA Flickr-PTR Conclusions We introduce

11. In this work we consider 3 popular AIA evaluation collections used by recent work [4] Corel5k [1] IAPR TC-12 [2] ESP Game [3] [1] Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. P. Duygulu et al. ECCV 02. [2] Labeling images with a computer game. L. von Ahn and L. Dabbish. CHI '04. [3] The IAPR TC-12 Benchmark- A New Evaluation Resource. M. Grubinger, et al. Visual Information Systems, 2006. [4] Baselines for Image Annotation. A. Makadia, V. Pavlovic and S. Kumar. IJCV 2010 Automatic Image Annotation

12. What are the problems with previous automatic image annotation collections?

13. Too many collections There needs to a be a single, openly available collection to reproduce experiments.

14. What are the problems with previous automatic image annotation collections? Too many collections There needs to a be a single, openly available collection to reproduce experiments. Annotation Ambiguity Collections use many synonyms in the annotation of images. e.g. usa/america etc.

15. What are the problems with previous automatic image annotation collections? Too many collections There needs to a be a single, openly available collection to reproduce experiments. Annotation Ambiguity Collections use many synonyms in the annotation of images. e.g. usa/america etc. Unnormalized Models are able to exploit popular tags by promoting them, increasing performance measures.

16. What are the problems with previous automatic image annotation collections? Too many collections There needs to a be a single, openly available collection to reproduce experiments. Annotation Ambiguity Collections use many synonyms in the annotation of images. e.g. usa/america etc. Unnormalized Models are able to exploit popular tags by promoting them, increasing performance measures. Low Image Quality Models are often tested on small, low quality image collections.

17. What are the problems with previous automatic image annotation collections? Too many collections There needs to a be a single, openly available collection to reproduce experiments. Annotation Ambiguity Collections use many synonyms in the annotation of images. e.g. usa/america etc. Unnormalized Models are able to exploit popular tags by promoting them, increasing performance measures. Low Image Quality Models are often tested on small, low quality image collections. Lack of Meta-data Despite the increase in research considering time/location etc, these collections dont include these.

18. What are the problems with previous automatic image annotation collections? Too many collections There needs to a be a single, openly available collection to reproduce experiments. Annotation Ambiguity Collections use many synonyms in the annotation of images. e.g. usa/america etc. Unnormalized Models are able to exploit popular tags by promoting them, increasing performance measures. Low Image Quality Models are often tested on small, low quality image collections. Lack of Meta-data Despite the increase in research considering time/location etc, these collections dont include these. Lack of diversity Collections often contain images taken on the same camera, at the same place by the same user.

19. What are the problems with previous automatic image annotation collections? Too many collections There needs to a be a single, openly available collection to reproduce experiments. Annotation Ambiguity Collections use many synonyms in the annotation of images. e.g. usa/america etc. Unnormalized Models are able to exploit popular tags by promoting them, increasing performance measures. Low Image Quality Models are often tested on small, low quality image collections. Lack of Meta-data Despite the increase in research considering time/location etc, these collections dont include these. Lack of diversity Collections often contain images taken on the same camera, at the same place by the same user. Location Tags Locations, such as usa, are impossible to identify, however, these tags are often included in ground truths.

20. What are the problems with previous automatic image annotation collections? Too many collections There needs to a be a single, openly available collection to reproduce experiments. Annotation Ambiguity Collections use many synonyms in the annotation of images. e.g. usa/america etc. Unnormalized Models are able to exploit popular tags by promoting them, increasing performance measures. Low Image Quality Models are often tested on small, low quality image collections. Lack of Meta-data Despite the increase in research considering time/location etc, these collections dont include these. Lack of diversity Collections often contain images taken on the same camera, at the same place by the same user. Location Tags Locations, such as usa, are impossible to identify, however, these tags are often included in ground truths. Copyright Corel is bound by copyright making distribution difficult.

21. 1. Problems Annotation Ambiguity All three collection ground truths contain: Synonyms (e.g. america/usa) Visually identical classes (e.g. sea/ocean) Corel5k IAPR TC-12 ESP Game

22. 1. Problems Annotation Ambiguity All three collection ground truths contain: Synonyms (e.g. america/usa) Visually identical classes (e.g. sea/ocean) To demonstrate this, we cluster tags which share a common WordNet synonym set (removing irrelevant matches manually). Corel5k IAPR TC-12 ESP Game

23. Corel polar/arctic ocean/sea 36 of 374 tags ice/frost

24. Corel polar/arctic ocean/sea ESP baby/child child/kid home/house 37 of 291 tags 36 of 374 tags ice/frost

25. Corel polar/arctic ocean/sea ESP baby/child child/kid home/house IAPR woman/adult bush/shrub rock/stone 37 of 291 tags 26 of 269 tags 36 of 374 tags ice/frost

26. 31% of photos in the Corel collection contain at least 1 ambiguous tag.

27. 31% of photos in the Corel collection contain at least 1 ambiguous tag. 25% of photos in the ESP collection contain at least 1 ambiguous tag.

28. 31% of photos in the Corel collection contain at least 1 ambiguous tag. 25% of photos in the ESP collection contain at least 1 ambiguous tag. sixty three% of photos in the IAPR collection contain at least 1 ambiguous tag.

29. Annotations: sea, usa, sky, chair Test image #1

30. Annotations: sea, usa, sky, chair Annotation Model #1 sea, usa, blue, water, red Test image #1 Precision 0.4 Suggestions

31. Annotations: sea, usa, sky, chair Annotation Model #1 Annotation Model #2 sea, usa, blue, water, red Test image #1 ocean, america, blue, water, red Precision 0.4 0 Suggestions

32. Annotations: sea, usa, sky, chair Annotation Model #1 Annotation Model #2 sea, usa, blue, water, red Test image #1 ocean, america, blue, water, red Precision 0.4 0 Suggestions So why do we penalize a system which treats these concepts differently? It is impossible to tell from an images pixels, whether it is of the sea or of the ocean.

33. 2. Problems Unnormalised Collections By nature, the classes used in image collections follow a long tail distribution i.e. there exist a few popular tags and many unpopular tags This causes problems: 1.Selection Bias: Popular tags exist in more training and test images. Therefore, annotation models are more likely to test on popular classes. 2.Prediction Bias: Popular tags occur in more test images. Therefore, annotation models can potentially cheat by promoting only popular tags, instead of making predictions based purely on the pixels. Corel5k IAPR TC-12 ESP Game

34. 2. Problems Unnormalised Collections By nature, the classes used in image collections follow a long tail distribution i.e. there exist a few popular tags and many unpopular tags This causes problems: 1.Selection Bias: Popular tags exist in more training and test images. Therefore, annotation models are more likely to test on popular classes. 2.Prediction Bias: Popular tags occur in more test images. Therefore, annotation models can potentially cheat by promoting only popular tags, instead of making predictions based purely on the pixels. Corel5k IAPR TC-12 ESP Game

35. 2. Problems Unnormalised Collections By nature, the classes used in image collections follow a long tail distribution i.e. there exist a few popular tags and many unpopular tags This causes problems: 1.Selection Bias: Popular tags exist in more training and test images. Therefore, annotation models are more likely to test on popular classes. 2.Prediction Bias: Popular tags occur in more test images. Therefore, annotation models can potentially cheat by promoting only popular tags, instead of making predictions based purely on the pixels. Corel5k IAPR TC-12 ESP Game

36. Tags # Images To demonstrate this prediction bias (i.e. where annotation models can cheat by promoting popular tags) We annotate each collection using the annotation model described in [6]. We split the vocabulary into 3 subsets of popular, medium frequency and unpopular tags. For each, we suggest only the tags in each subset. [6] Baselines for Image Annotation. A. Makadia, V. Pavlovic and S. Kumar. IJCV 2010 Corel5k IAPR TC-12 ESP Game

37. Popular tags Tags # Images To demonstrate this prediction bias (i.e. where annotation models can cheat by promoting popular tags) We annotate each collection using the annotation model described in [6]. We split the vocabulary into 3 subsets of popular, medium frequency and unpopular tags. For each, we suggest only the tags in each subset. [6] Baselines for Image Annotation. A. Makadia, V. Pavlovic and S. Kumar. IJCV 2010 Corel5k IAPR TC-12 ESP Game

38. Popular tags Medium Frequency Tags # Images To demonstrate this prediction bias (i.e. where annotation models can cheat by promoting popular tags) We annotate each collection using the annotation model described in [6]. We split the vocabulary into 3 subsets of popular, medium frequency and unpopular tags. For each, we suggest only the tags in each subset. [6] Baselines for Image Annotation. A. Makadia, V. Pavlovic and S. Kumar. IJCV 2010 Corel5k IAPR TC-12 ESP Game

39. Popular tags Medium Frequency Unpopular Tags Tags # Images To demonstrate this prediction bias (i.e. where annotation models can cheat by promoting popular tags) We annotate each collection using the annotation model described in [6]. We split the vocabulary into 3 subsets of popular, medium frequency and unpopular tags. For each, we suggest only the tags in each subset. [6] Baselines for Image Annotation. A. Makadia, V. Pavlovic and S. Kumar. IJCV 2010 Corel5k IAPR TC-12 ESP Game

41. Ultimately, higher annotation accuracy can be achieved by suggesting only popular tags.

42. 3. Problems Image quality/size Despite the increase in Hadoop clusters & computational power, many works still test on small collections of low quality images. CollectionSize (avg dimension)# Images Corel160px5,000 ESP156px22,000 IAPR417px20,000 Corel5k IAPR TC-12 ESP Game

43. 4. Problems Lack of meta-data Many recent works have focussed on the exploitation of various meta-data [7,8] e.g. Time, Location, Camera, User CollectionTimeLocation CorelXX ESPXX IAPR [7] On contextual photo tag recommendation. P McParlane, Y Moshfeghi, J Jose SIGIR 2013 [8] Beyond co-occurrence: discovering and visualizing tag relationships from geo-spatial. H Zhang et al. WSDM 2012 Corel5k IAPR TC-12 ESP Game

44. 5. Problems Lack of diversity Images in each collection are often taken by the same user, in the same place, of the same scene/object, using the same camera. This leads to natural clustering in image collections, making annotation easier due to high inter-cluster visual similarity. Further there are duplicate images in the test and train sets, also making annotation easier. Corel5k IAPR TC-12 ESP Game

46. 6. Problems Identifying Location Identifying location (even high level) within an image is often difficult or sometimes impossible. Despite this, two of the three collections contain images annotated with locations (e.g. usa). Given this image, would you know where it was taken? Corel5k IAPR TC-12 ESP Game

47. 6. Problems Identifying Location Identifying location (even high level) within an image is often difficult or sometimes impossible. Despite this, two of the three collections contain images annotated with locations (e.g. usa). Given this image, would you know where it was taken? Annotations: sea, usa, sky, chair If not, how can we expect an annotation model to predict the annotation usa? Corel5k IAPR TC-12 ESP Game

48. 7. Problems Copyright An evaluation collection should at least be free and distributable. Unfortunately, the Corel collection is commercial and bound by copyright. Corel5k IAPR TC-12 ESP Game

49. Motivation for annotating images Problems with existing automatic image annotation collections Problems with existing photo tag recommendation collections Flickr-AIA Flickr-PTR Conclusions We introduce

50. Flickr-AIA contains 312,000 images from Flickr built with AIA evaluation in mind.

51. Openly available Using Flickr images under the creative commons license

52. Flickr-AIA contains 312,000 images from Flickr built with AIA evaluation in mind. Openly available Using Flickr images under the creative commons license Meta-data Includes extensive location, user and time meta- data.

53. Flickr-AIA contains 312,000 images from Flickr built with AIA evaluation in mind. Openly available Using Flickr images under the creative commons license Meta-data Includes extensive location, user and time meta- data. Diverse image set We search images for 2,000 WordNet categories & limit the number of images for each user.

54. Flickr-AIA contains 312,000 images from Flickr built with AIA evaluation in mind. Openly available Using Flickr images under the creative commons license Meta-data Includes extensive location, user and time meta- data. Diverse image set We search images for 2,000 WordNet categories & limit the number of images for each user. High quality The dimension of each image is 719px on average.

55. Flickr-AIA contains 312,000 images from Flickr built with AIA evaluation in mind. Openly available Using Flickr images under the creative commons license Meta-data Includes extensive location, user and time meta- data. Diverse image set We search images for 2,000 WordNet categories & limit the number of images for each user. High quality The dimension of each image is 719px on average. No Location tags We use WordNet to remove location tags from image ground truths (e.g. scotland).

56. Flickr-AIA contains 312,000 images from Flickr built with AIA evaluation in mind. Openly available Using Flickr images under the creative commons license Meta-data Includes extensive location, user and time meta- data. Diverse image set We search images for 2,000 WordNet categories & limit the number of images for each user. High quality The dimension of each image is 719px on average. No Location tags We use WordNet to remove location tags from image ground truths (e.g. scotland). Resolved Ambiguity Tags which are synonyms (e.g. usa/america) are merged based on WordNet synonym sets.

57. Flickr-AIA contains 312,000 images from Flickr built with AIA evaluation in mind. Openly available Using Flickr images under the creative commons license Meta-data Includes extensive location, user and time meta- data. Diverse image set We search images for 2,000 WordNet categories & limit the number of images for each user. High quality The dimension of each image is 719px on average. No Location tags We use WordNet to remove location tags from image ground truths (e.g. scotland). Resolved Ambiguity Tags which are synonyms (e.g. usa/america) are merged based on WordNet synonym sets. Normalized Aside from the normal ground truth, we include a normalised ground truth containing only medium frequency tags.

58. Motivation for annotating images Problems with existing automatic image annotation collections Problems with existing photo tag recommendation collections Flickr-AIA Flickr-PTR Conclusions We introduce

59. In this work we consider collection used in 2 popular photo tag recommendation works. Photo Tag Recommendation [5] Flickr Tag Recommendation based on Collective Knowledge. B. Sigurbjornsson and R. van Zwol. WWW '08. [6] Personalized, Interactive Tag Recommendation for Flickr. N. Garg and I. Weber. ACM RecSys '08. Sigurbjornsson [5] Garg [6]

60. 1. Problems Ground Truth Sigurbjornsson use a small collection of images which have their ground truths crowdsourced. For photo tag recommendation, however, many aspects that users would tag are often not explicit (e.g. locations, dates etc). Therefore, these annotations are missed using crowdsourcing. Sigurbjornsson Garg

61. Crowdsourced Worker footballred teamblue england grass saturday Comparing Annotations Crowdsourced vs tags added by the user.

62. Crowdsourced footballred teamblue england grass saturday Comparing Annotations Crowdsourced vs tags added by the user. User Tags footballred teamblue scotland hamilton accies artificial grass dunfermline sundaynew douglas park

63. Crowdsourced footballred teamblue england grass saturday Comparing Annotations Crowdsourced vs tags added by the user. User Tags footballred teamblue scotland hamilton accies artificial grass dunfermline sundaynew douglas park

64. 2. Problems Synonymous Tags One of the problems with using user tags, however, is that users often use many synonyms to annotate images. Sigurbjornsson Garg

65. Annotations: newyork, ny, nyc, newyorkcity, york, timessquare Test image #1

66. Annotations: newyork, ny, nyc, newyorkcity, york, timessquare Annotation Model #1 ny, nyc, newyork, york, city Test image #1 Precision 0.8 Suggestions

67. Annotations: newyork, ny, nyc, newyorkcity, york, timessquare Annotation Model #1 Annotation Model #2 ny, nyc, newyork, york, city Test image #1 ny, timessquare, people, cab, empire Precision 0.8 0.4 Suggestions

68. Annotations: newyork, ny, nyc, newyorkcity, york, timessquare Annotation Model #1 Annotation Model #2 ny, nyc, newyork, york, city Test image #1 ny, timessquare, people, cab, empire Precision 0.8 0.4 Suggestions

69. 3. Problems Free distribution Existing collections [6,7] for photo tag recommendation were never released making comparable experiments difficult. [6] Personalized, Interactive Tag Recommendation for Flickr. N. Garg and I. Weber. ACM RecSys '08. [7] Flickr Tag Recommendation based on Collective Knowledge. B. Sigurbjornsson and R. van Zwol. WWW '08. Sigurbjornsson Garg

70. Motivation for annotating images Problems with existing automatic image annotation collections Problems with existing photo tag recommendation collections Flickr-AIA Flickr-PTR Conclusions We introduce

71. Flickr-PTR contains details of 2,000,000 images from Flickr built with PTR evaluation in mind.

72. Openly available Using Flickr images under the creative commons license

73. Flickr-PTR contains details of 2,000,000 images from Flickr built with PTR evaluation in mind. Openly available Using Flickr images under the creative commons license Clustered User Tags Using a crowdsourced experiment which asked user to group related tags to overcome the problem of synonyms.

74. To overcome synonyms in image annotations we took out an crowdsourced experiment which grouped the synonyms or the tags which refer to the same aspect.

75. Conclusions This work highlighted: 7 problems with existing AIA collections (Corel, ESP, IAPR) 3 problems with existing PTR collections (Sigur, Garg) With this in mind, we introduce two new, freely available image collections: Flickr-AIA 312,000 Flickr images Flickr-PTR 2,000,000 Flickr images Automatic image annotation evaluation Photo tag recommendation evaluation

76. These collections are available at: http://dcs.gla.ac.uk/~philip/ Thanks for listening! [1] Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. P. Duygulu et al. ECCV 02. [2] Labeling images with a computer game. L. von Ahn and L. Dabbish. CHI '04. [3] The IAPR TC-12 Benchmark- A New Evaluation Resource. M. Grubinger, et al. Visual Information Systems, 2006. [4] Flickr Tag Recommendation based on Collective Knowledge. B. Sigurbjornsson and R. van Zwol. WWW '08. [5] Personalized, Interactive Tag Recommendation for Flickr. N. Garg and I. Weber. ACM RecSys '08. [6] Baselines for Image Annotation. A. Makadia, V. Pavlovic and S. Kumar. IJCV 2010 [7] On contextual photo tag recommendation. P McParlane, Y Moshfeghi, J Jose SIGIR 2013 [8] Beyond co-occurrence: discovering and visualizing tag relationships from geo-spatial. H Zhang et al. WSDM 2012