Fashion-focused creative commons social dataset

ABSTRACT

In this work, we present a fashion-focused Creative Commons dataset, which is designed to contain a mix of general images as well as a large component of images that are focused on fashion (i.e., relevant to particular clothing items or fashion accessories). The dataset contains 4810 images and related metadata. Furthermore, a ground truth on image's tags is presented. Ground truth generation for large-scale datasets is a necessary but expensive task. Traditional expert based approaches have become an expensive and non-scalable solution. For this reason, we turn to crowdsourcing techniques in order to collect ground truth labels; in particular we make use of the commercial crowdsourcing platform, Amazon Mechanical Turk (AMT). Two different groups of annotators (i.e., trusted annotators known to the authors and crowdsourcing workers on AMT) participated in the ground truth creation. Annotation agreement between the two groups is analyzed. Applications of the dataset in different contexts are discussed. This dataset contributes to research areas such as crowdsourcing for multimedia, multimedia content analysis, and design of systems that can elicit fashion preferences from users.

References

1. M. Larson, M. Soleymani, M. Eskevich, P. Serdyukov, R. Ordelman and G. Jones, The Community and the Crowd: Multimedia Benchmark Dataset Development, in IEEE Multimedia, 2012.
2. A. J. Quinn and B. B. Bederson, Human computation: a survey and taxonomy of a growing field, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2011.
3. S. Nowak and S. Ruger, How reliable are annotations via crowdsourcing? a study about inter-annotator agreement for multi-label image annotation, in The 11th ACM International Conference on Multimedia Information Retrieval (MIR), Philadelphia, USA, 2010.
4. L. Ahn, Games with a purpose, IEEE Computer Society, vol. 39, pp. 92--96, 2006.
5. L. Ahn and L. Dabbish, Labeling images with a computer game, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '04), 2004.
6. S. Li, J. Feng, Z. Song, T. Zhang, H. Lu, C. Xu and S. Yan, Hi, Magic Closet, Tell Me What to Wear, in Proceeding of International Conference of ACM MM, Nara, Japan, 2012.
7. T. Iwata, S. Watanabe and H. Sawada, Fashion coordinates recommender system using photographs from fashion magazines, in In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence, 2011.
8. M. Fukuda and Y. Nakatani, What to Wear in Different Situations? A New Approach to a Fashion Coordinate Support System, in Proceedings of the World Congress on Engineering and Computer Science, 2011.
9. R. Sakurai and J.-H. Lee, People and Clothes Recognition based on Topic Model Integration (SII), in EEE/SICE International Symposium on System, 2011.
10. K. Yamaguchi, H. Kiapour, L. Ortiz and T. Berg, Parsing clothing in fashion photographs, in IEEE Conference on Computer Vision and Pattern Recognition, 2012.
11. J. S. Pedro, S. Siersdorfer and M. Sanderson, Content redundancy in YouTube and its application to video tagging, in ACM Trans. Inf. Syst., 2011.
12. K. Filippova and K. Hall, Improved video categorization from text metadata and user comments, in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information (SIGIR-2011), 2011.
13. S. V. Chelaru, C. Orellana-Rodriguez and I. S. Altingövde, Can Social Features Help Learning to Rank YouTube Videos?, in 13th International Conference on Web Information System Engineering, 2012.
14. A. Cox, P. Clough and S. Siersdorfer, "Developing metrics to characterize Flickr groups," Am. Soc. Inf. Sci. Technol., vol. 62, pp. 493--506, 2011.
15. J. San Pedro, T. Yeh and N. Oliver, Leveraging user comments for aesthetic aware image search reranking, in WWW'12, 2012.
16. C. Eickhoff and C. Vries, Increasing cheat robustness of crowdsourcing tasks, Information Retrieval Journal, 2012.
17. P. Fraternali, M. Tagliasacchi and D. Martinenghi, The CUBRIK project: human-enhanced time-aware multimedia search, in WWW 2012 -- European Projects Track, 2012.
18. J. Randolph, Free-marginal multirater kappa: An alternative to Fleiss' fixed-marginal multirater kappa, in Joensuu University Learning and Instruction Symposium, 2005.
19. L. v. Ahn, R. Liu and a. M. Blum, Peekaboom:a game for locating objects in images, in In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '06), 2006.
20. L. Galli, P. Fraternali, J. Novak, D. Martinenghi and M. Tagliasacchi, A Draw-and-Guess Game to Segment Images, in ASE International Conference on Social Computing (SocialCom), 2012.
21. J. A. Noble, Minority voices of crowdsourcing: why we should pay attention to every member of the crowd, in In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work Companion (CSCW '12), New York, USA, 2012.

Index Terms

• Applied computing

  • Computers in other domains

    • Digital libraries and archives

• Information systems

  • Information retrieval

    • Document representation

      • Document collection models

  • Information systems applications

    • Digital libraries and archives