Quorum Based Image Retrieval in Large Scale Visual Sensor Networks

  • Stojan Milovanovic
  • Milos Stojmenovic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7363)


A recent publication by [SPKK] introduces a framework and set of rules by which object recognition can work on a visual sensor network. Extracted features of the detected object are flooded (with reduced dimensionality at each hop) in the network. The Sensor will match the corresponding feature of the new object with a locally stored one, and send the query on the backward link toward the original detector for matching. Based on their framework we introduce an algorithm which attempts to minimize the number of messages passed within the network when performing an image retrieval task. Extracted features are distributed along a row, while query matching progresses along a column. We compare our results to the algorithm proposed by [SPKK] and achieve fewer transmissions in the retrieval step, and avoid flooding in the pre-processing phase. We expand our algorithm by constructing an information mesh of multiple detections of the same object, to achieve matching with the nearest copy. We also propose a novel feature reduction method, by diving the image into k2 subimages, and extracting features in each subimage. This allows replacing histogram based features with a wide range of other options.


visual sensor networks computer vision object recognition 


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  1. [AB]
    Arth, C., Bischof, H.: Real–time object recognition using local features on a dsp–based embedded system. Journal of Real–time Image Processing 3(4), 233–253 (2008)CrossRefGoogle Scholar
  2. [ABL]
    Arth, C., Bischof, H., Leistner, C.: TRICam - an embedded platform for remote traffic surveillance. In: Conference on Computer Vision and Pattern Recognition Workshop (2006)Google Scholar
  3. [CDR]
    Cheng, Z., Devarajan, D., Radke, R.J.: Determining vision graph for distributed camera networks using feature digests. EURASIP Journal on Applied Signal Processing 2007(1) (2007)Google Scholar
  4. [CWM]
    Charfi, Y., Wakamiya, N., Murata, M.: Trade-off between reliability and energy cost for content–rich data transmission in wireless sensor networks. In: 3rd International Conference on Broadband Communications, Networks and Systems, pp. 1–8 (2006)Google Scholar
  5. [CWM]
    Charfi, Y., Wakamiya, N., Murata, M.: Challenging issues in visual sensor networks. IEEE Wireless Communications 6(2), 44–49 (2009)CrossRefGoogle Scholar
  6. [DR]
    Devarajan, D., Radke, R.J.: Calibrating distributed camera networks using belief propagation. EURASIP Journal on Applied Signal Processing 2007(1), 221 (2007)Google Scholar
  7. [FBBS]
    Fleck, S., Busch, F., Biber, P., Strasser 3d, W.: surveillance - a distributed network of smart cameras for real-time tracking and its visualization in 3d. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, p. 118 (2006)Google Scholar
  8. [GB]
    Gilbert, A., Bowden, R.: Incremental, scalable tracking of objects inter camera. Journal of Computer Vision and Image Understanding 111(1), 43–58 (2008)CrossRefGoogle Scholar
  9. [LDK]
    Lecuire, V., Duran-Faundez, C., Krommenacker, N.: Energy-efficient image transmission in sensor networks. International Journal of Sensor Networks 4(1), 37–47 (2008)CrossRefGoogle Scholar
  10. [LKZ]
    Luh, W., Kundur, D., Zourntos, T.: A novel distributed privacy paradigm for visual sensor networks based on sharing dynamical systems. EURASIP Journal on Advances in Signal Processing 2007(1) (2007)Google Scholar
  11. [LLC]
    Lu, Q., Luo, W., Wang, J., Chen, B.: Low-complexity and energy efficient image compression scheme for wireless sensor networks. Computer Networks 52(13), 2594–2603 (2008)zbMATHCrossRefGoogle Scholar
  12. [LSS2]
    Li, X., Santoro, N., Stojmenovic, I.: Localized Distance-Sensitive Service Discovery in Wireless Sensor and Actor Networks. IEEE Transactions on Computers 58(9), 1275–1288 (2009)MathSciNetCrossRefGoogle Scholar
  13. [PCPGM]
    Patricio, M., Carbo, J., Perez, O., Garcia, J., Molina, J.M.: Multi–agent framework in visual sensor networks. EURASIP Journal on Advances in Signal Processing 2007(1) (2007)Google Scholar
  14. [QKRBS]
    Quaritsch, M., Kreuzthaler, M., Rinner, B., Bischof, H., Strobl, B.: Autonomous multicamera tracking on embedded smart cameras. EURASIP Journal on Embedded Systems (2007)Google Scholar
  15. [SLJ]
    Stojmenovic, I., Liu, D., Jia, X.: A scalable quorum based location service in ad hoc and sensor networks. International Journal of Communication Networks and Distributed Systems 1(1), 71–94 (2008); invited paperCrossRefGoogle Scholar
  16. [SPKK]
    Sulic, V., Pers, J., Kristan, M., Kovacic, S.: IEEE Transactions on Circuits and Systems for Video Technology 21(7), 903–916 (2011)CrossRefGoogle Scholar
  17. [WA]
    Wu, H., Abouzeid, A.: Energy efficient distributed image compression in resource-constrained multihop wireless networks. Computer Communications 28(14), 1658–1668 (2005)CrossRefGoogle Scholar
  18. [WA2]
    Wu, H., Abouzeid, A.A.: Error resilient image transport in wireless sensor networks. Computer Networks 50(15), 2873–2887 (2006)zbMATHCrossRefGoogle Scholar
  19. [YSV]
    Yu, W., Sahinoglu, Z., Vetro, A.: Energy efficient JPEG 2000 image transmission over wireless sensor networks. In: IEEE Global Telecommunications Conference, GLOBECOM 2004, pp. 2738–2743 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stojan Milovanovic
    • 1
  • Milos Stojmenovic
    • 1
  1. 1.Singidunum UniversityBelgradeSerbia

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