Multimedia Tools and Applications

, Volume 71, Issue 1, pp 263–278 | Cite as

Converting image to a gateway to an information portal for digital signage

  • Young-Hwan Choi
  • Daehoon Kim
  • Seungmin Rho
  • Eenjun HwangEmail author


Digital signage has recently emerged as a new channel for communicating with people in diverse domains such as advertising, shopping mall and public service. In this paper, we propose a novel data fusion method for converting an advertisement image into a gateway to an information portal based on steganography technology for digital signage. We make the information portal very flexible just by changing the link or by organizing the contents dynamically. Typical contents include product information and summary of user evaluation. To implement this scheme, we first register products of interest with their representative features and quick response (QR) code. The representative points are used for detecting products in images and their QR code is embedded into the detected product area using our steganography technique. We implement a prototype system based on our scheme, and show its effectiveness through extensive experiments.


Digital signage Object recognition SURF Local feature Feature descriptor Steganography Histogram shifting Review analyzer 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012-0007202).


  1. 1.
    Agerri R, García-Serrano A (2010) Q-WordNet: Extracting polarity from WordNet senses. Proceedings of the Seventh conference on International Language Resources and Evaluation, Retrieved May 25, 2010Google Scholar
  2. 2.
    Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the Seventh conference on International Language Resources and Evaluation, Retrieved May 25, 2010Google Scholar
  3. 3.
    Bay H, Tuytelaars T, VanGool L (2006). SURF: Speeded Up Robust Features. In ECCV (1), pp 404–417Google Scholar
  4. 4.
    Bradley MM, Lang PJ (1999) Affective norms for English words (ANEW): Instruction manual and affective ratings, Technical Report C-1, The Center for Research in Psychophysiology. University of Florida, GainesvilleGoogle Scholar
  5. 5.
    Che-Wei L, Wen-Hsiang T (2010) A lossless data hiding method by histogram shifting based on an adaptive block division scheme. Pattern Recognition and Machine Vsion, River Publishers, Aalborg, pp 1–14Google Scholar
  6. 6.
    Dave K, Lawrence S, Pennock D (2003) “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews,” Proc. of 12th Int. Conf. on World Wide Web, pp 519–528Google Scholar
  7. 7.
    Fridrich J, Goljan M, Du R (2001) “Invertible Authentication”, Proc. SPIE Security and Watermarking of Multimedia Contents, pp 197–208Google Scholar
  8. 8.
    Islam MI, Begum N, Alam M, Amin MR (2010) Fingerprint detection using canny filter and DWT, a new approach. J Inf Process Syst 6(4):511–520Google Scholar
  9. 9.
    Johnson NF, Jajodia S (1998) Exploring steganography: seeing the unseen. Computer Practices 31:26–34Google Scholar
  10. 10.
    Kim D, Rho S, Hwang E (2012) Local feature-based multi-object recognition scheme for surveillance. Eng Appl Artif Intel. doi: 10.1016/j.engappai.2012.03.005
  11. 11.
    Kudo T, Matsumoto Y (2004) A boosting algorithm for classification of semi-structured text. Proceedings of 9th Conference on Empirical Methods in Natural Language Processing, July 25–26; Barcelona, SpainGoogle Scholar
  12. 12.
    Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110CrossRefGoogle Scholar
  13. 13.
    Matas, J., Chum, O., Martin, U., Pajdla, T., 2002. Robust Wide baseline stereo from maximally stable extremal regions. In British Machine Vision Conference, pp. 384–393.Google Scholar
  14. 14.
    Mikolajczyk K, Schmid C (2004) Scale & affine invariant interest point detectors. Int J Comput Vision 60(1):63–86CrossRefGoogle Scholar
  15. 15.
    Mikolajczyk K, Schmid C (2005) Performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630CrossRefGoogle Scholar
  16. 16.
    Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, VanGool L (2005) A comparison of affine region detectors. Int J Comput Vision 65(1–2):43–72CrossRefGoogle Scholar
  17. 17.
    Ni Z, Shi YQ, Ansari N, Su W (2006) Reversible data hiding. IEEE Transactions on Circuits and Systems for Video Technology 16(3):354–361CrossRefGoogle Scholar
  18. 18.
    Tsai CL, Chiang HF, Fan KC, Chung CD (2005) Reversible data hiding and lossless reconstruction of binary images using pair-wise logical computation mechanism. Pattern Recognition 38(11):1993–2006CrossRefGoogle Scholar
  19. 19.
    Tsai P, Hu YC, Yeh HL (2009) Reversible image hiding scheme using predictive coding and histogram shifting. Signal Processing 89:1129–1143CrossRefzbMATHGoogle Scholar
  20. 20.
    Wang J, Ni J (2010) “A fast performance estimation scheme for histogram shifting based multi-layer embedding,” Proc. Of IEEE 17th Int. Conf. on Image Processing, pp 26–29Google Scholar
  21. 21.
    Wiebe J, Riloff E (2005) Creating subjective and objective sentence classifiers from unannotated texts. Proceedings of 6th International Conference on Computational Linguistics and Intelligent Text Processing, February 13–19; Mexico City, MexicoGoogle Scholar
  22. 22.
    Yang CH, Weng CY, Wang SJ, Sun HM (2008) Adaptive data hiding in edge areas of images with spatial LSB domain systems. IEEE Trans Inf Forens Security 3(3):488–497CrossRefGoogle Scholar
  23. 23.
    Yi J, Nasukawa T, Bunescu R, Niblack W (2003) Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. Proceedings of the 3rd IEEE International Conference on Data Mining, November 19–22; Florida, USAGoogle Scholar
  24. 24.
    Zhi-Hui Wang, Chin-Feng Lee, Ching-Yun Chang (2012) “Histogram shifting imitated reversible data hiding,” The Journal of Systems and Software, Article in pressGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Young-Hwan Choi
    • 1
  • Daehoon Kim
    • 1
  • Seungmin Rho
    • 2
  • Eenjun Hwang
    • 1
    Email author
  1. 1.School of Electrical EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Division of Information and CommunicationBaekseok UniversityCheonan-cityRepublic of Korea

Personalised recommendations