Picture News Collection: A Dataset for Automatic Picture News Thumbnail Selection

  • Yi-Kun Tang
  • Heyan HuangEmail author
  • Xuewen Shi
  • Xian-Ling Mao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)


Picture news has become more and more popular among online news in recent years. As the first impression to viewers, thumbnail plays a very important role in picture news. However, it is time consuming to manually select thumbnails for a huge amount of picture news. In this paper, we introduce a new task of automatic picture news thumbnail selection. Given a piece of picture news containing a set of images, this task is to select several appropriate images from the picture news as candidate thumbnails. To this end, we present a large publicly available image dataset for this task, called Picture News Collection(The Picture News Collection 0.1 version can be publicly available online at The Picture News Collection contains more than 4 million images of 347,731 picture news from two famous news websites, Sina News and NetEase News. Selecting good enough thumbnails is complicated and needs to consider many aspects, such as attraction, hot topics, content integrity, etc. In order to select appropriate candidate thumbnails, we propose an attention-based thumbnail selection model, and the experimental results comparing with three image classification based baselines show that our proposed methods outperform the baselines. We introduce the automatic picture news thumbnail selection task and the dataset to encourage further studies of this challenge.


Automatic picture news thumbnail selection Picture News Collection Image selection 



We thank all anonymous reviewers for their valuable comments. This work is supported by National Key R&D Plan (No. 2016QY03D0602), BIGKE (No. 20160754021), NSFC (No. 61772076), NSFB (No. Z181100008918002), Major Project of Zhijiang Lab (No. 2019DH0ZX01), and CETC (No. w-2018018).


  1. 1.
    Aiello, L.M., Schifanella, R., Redi, M., Svetlichnaya, S., Liu, F., Osindero, S.: Beautiful and damned. combined effect of content quality and social ties on user engagement. IEEE Trans. Knowl. Data Eng. 29(12), 2682–2695 (2017)CrossRefGoogle Scholar
  2. 2.
    Aydın, T.O., Smolic, A., Gross, M.: Automated aesthetic analysis of photographic images. IEEE Trans. Visual Comput. Graphics 21(1), 31–42 (2015)CrossRefGoogle Scholar
  3. 3.
    Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
  4. 4.
    Ceroni, A.: Personal photo management and preservation. In: Mezaris, V., Niederée, C., Logie, R.H. (eds.) Personal Multimedia Preservation. SSCC, pp. 279–314. Springer, Cham (2018). Scholar
  5. 5.
    Ceroni, A., Solachidis, V., Niederée, C., Papadopoulou, O., Kanhabua, N., Mezaris, V.: To keep or not to keep: an expectation-oriented photo selection method for personal photo collections. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 187–194. ACM (2015)Google Scholar
  6. 6.
    Ceroni, A., Solachidis, V., Niederée, C., Papadopoulou, O., Mezaris, V.: Expo: an expectation-oriented system for selecting important photos from personal collections. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 452–456. ACM (2017)Google Scholar
  7. 7.
    Fu, M., et al.: Learning personalized expectation-oriented photo selection models for personal photo collections. In: 2015 IEEE International Conference on Multimedia & Expo Workshops, pp. 1–6. IEEE (2015)Google Scholar
  8. 8.
    Gao, Y., Zhang, T., Xiao, J.: Thematic video thumbnail selection. In: 2009 16th IEEE International Conference on Image Processing, pp. 4333–4336. IEEE (2009)Google Scholar
  9. 9.
    Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D CNNS retrace the history of 2D CNNS and imagenet? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6546–6555 (2018)Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  11. 11.
    Kuzovkin, D., Pouli, T., Cozot, R., Le Meur, O., Kervec, J., Bouatouch, K.: Image selection in photo albums. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 397–404. ACM (2018)Google Scholar
  12. 12.
    Li, C., Loui, A.C., Chen, T.: Towards aesthetics: a photo quality assessment and photo selection system. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 827–830. ACM (2010)Google Scholar
  13. 13.
    Liu, W., Mei, T., Zhang, Y., Che, C., Luo, J.: Multi-task deep visual-semantic embedding for video thumbnail selection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3707–3715 (2015)Google Scholar
  14. 14.
    Marchesotti, L., Murray, N., Perronnin, F.: Discovering beautiful attributes for aesthetic image analysis. Int. J. Comput. Vision 113(3), 246–266 (2015)CrossRefGoogle Scholar
  15. 15.
    Rabbath, M., Sandhaus, P., Boll, S.: Automatic creation of photo books from stories in social media. ACM Trans. Multimedia Comput. Commun. Appl. 7(1), 27 (2011)Google Scholar
  16. 16.
    Seah, B.S., Bhowmick, S.S., Sun, A.: Prism: concept-preserving social image search results summarization. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 737–746. ACM (2014)Google Scholar
  17. 17.
    Sinha, P., Mehrotra, S., Jain, R.: Summarization of personal photologs using multidimensional content and context. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p. 4. ACM (2011)Google Scholar
  18. 18.
    Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: Advances in Neural Information Processing Systems, pp. 1857–1865 (2016)Google Scholar
  19. 19.
    Song, Y., Redi, M., Vallmitjana, J., Jaimes, A.: To click or not to click: automatic selection of beautiful thumbnails from videos. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 659–668. ACM (2016)Google Scholar
  20. 20.
    Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)Google Scholar
  21. 21.
    Walber, T.C., Scherp, A., Staab, S.: Smart photo selection: interpret gaze as personal interest. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2065–2074. ACM (2014)Google Scholar
  22. 22.
    Wang, Y., Han, B., Li, D., Thambiratnam, K.: Compact web video summarization via supervised learning. In: 2018 IEEE International Conference on Multimedia & Expo Workshops, pp. 1–4. IEEE (2018)Google Scholar
  23. 23.
    Zhang, W., Liu, C., Wang, Z., Li, G., Huang, Q., Gao, W.: Web video thumbnail recommendation with content-aware analysis and query-sensitive matching. Multimedia Tools Appl. 73(1), 547–571 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yi-Kun Tang
    • 1
    • 2
  • Heyan Huang
    • 1
    • 2
    Email author
  • Xuewen Shi
    • 1
    • 2
  • Xian-Ling Mao
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  2. 2.Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing ApplicationsBeijingChina

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