Content Based Detection of Popular Images in Large Image Databases

  • Martin Solli
  • Reiner Lenz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


We investigate the use of standard image descriptors and a supervised learning algorithm for estimating the popularity of images. The intended application is in large scale image search engines, where the proposed approach can enhance the user experience by improving the sorting of images in a retrieval result. Classification methods are trained and evaluated on real-world user statistics recorded by a major image search engine. The conclusion is that for many image categories, the combination of supervised learning algorithms and standard image descriptors results in useful popularity predictions.


Support Vector Machine Image Retrieval Image Category Scale Invariant Feature Transform Retrieval Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying aesthetics in photographic images using a computational approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 288–301. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Datta, R., Li, J., Wang, J.: Learning the consensus on visual quality for next-generation image management. In: 15th ACM Int. Conf. on Multimedia, MM 2007, Augsburg, pp. 533–536 (2007)Google Scholar
  3. 3.
    Datta, R., Joshi, D., Li, J., Wang, J.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2) (2008)Google Scholar
  4. 4.
    van Gemert, J.C., Veenman, C.J., Smeulders, A.W., Geusebroek, J.-M.: Visual word ambiguity. IEEE TPAMI 32, 1271–1283 (2010)CrossRefGoogle Scholar
  5. 5.
    Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 419–426 (2006)Google Scholar
  6. 6.
    Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. Computer Graphics Forum (Proceedings of Eurographics) 29, 469–478 (2010)CrossRefGoogle Scholar
  7. 7.
    Liu, L., Jin, Y., Wu, Q.: Realtime aesthetic image retargeting. In: Proc. of Eurographics WS on Computational Aesthetic in Graphics, Visualization, and Imaging, pp. 1–8 (2010)Google Scholar
  8. 8.
    Cohen-Or, D., Sorkine, O., Gal, R., Leyvand, T., Xu, Y.-Q.: Color harmonization. In: ACM SIGGRAPH 2006, vol. 25, pp. 624–630 (2006)Google Scholar
  9. 9.
    Huang, T.S., Dagli, C.K., Rajaram, S., Chang, E.Y.: Active Learning for Interactive Multimedia Retrieval. Proceedings of the IEEE 96(4), 648–667 (2008)CrossRefGoogle Scholar
  10. 10.
    Joachims, T.: Making large-scale support vector machine learning practical. In: Advances in Kernel Methods: Support Vector Learning, pp. 169–184 (1999)Google Scholar
  11. 11.
    Lin, H.T., Lin, C.J., Weng, R.C.: A note on platt’s probabilistic outputs for support vector machines. Mach. Learn. 68(3), 267–276 (2007)CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Zhang, D., Lu, G., Ma, W.-Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40(1), 262–282 (2007)CrossRefzbMATHGoogle Scholar
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    van de Sande, K.E., Gevers, T., Snoek, C.G.: Evaluating color descriptors for object and scene recognition. IEEE TPAMI 32, 1582–1596 (2010)CrossRefGoogle Scholar
  15. 15.
    Solli, M., Lenz, R.: Color based bags-of-emotions. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 573–580. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Martin Solli
    • 1
  • Reiner Lenz
    • 1
  1. 1.Media and Information Technology (MIT), Department of Science and Technology (ITN)Linköping UniversityNorrköpingSweden

Personalised recommendations