Abstract
Target classification, especially visual target classification, in complex situations is challenging for image recognition in wireless sensor networks (WSNs). The distributed and online learning for target classification is significant for highly-constrained WSNs. This paper presents a collaborative target classification algorithm for image recognition in WSNs, taking advantages of the collaboration for the data mining between multi-sensor nodes. The proposed algorithm consists of three steps, target detection and feature extraction are based on single-sensor node processing, whereas target classification is implemented by collaboration between multi-sensor nodes using collaborative support vector machines (SVMs). For conquering the disadvantages of inevitable missing rate and false rate in target detection, the proposed collaborative SVM adopts a robust mechanism for adaptive sample selection, which improves the incremental learning of SVM by just fusing the information from a selected set of wireless sensor nodes. Furthermore, a progressive distributed framework for collaborative SVM is also introduced for enhancing the collaboration between multi-sensor nodes. Experimental results demonstrate that the proposed collaborative target classification algorithm for image recognition can accomplish target classification quickly and accurately with little congestion, energy consumption and execution time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, D., Wong, K.D., Hu, Y.H., Sayeed, A.M.: Detection, Classification and Tracking of Targets. IEEE Signal Processing Magazine 19(2), 17–29 (2002)
Chong, D., Kumar, S.P.: Sensor Networks: Evolution, Opportunities and Challenges. Proceedings of the IEEE 91(8), 1247–1256 (2003)
Wang, T.-Y.: A Combined Decision Fusion and Channel Coding Scheme for Distributed Fault-Tolerant Classification in Wireless Sensor Networks. IEEE Transactions on Wireless Communications 5(7), 1695–1705 (2006)
D’Costa, A., Ramachandran, V., Sayeed, A.M.: Distributed Classification of Gaussian Space-Time Sources in Wireless Sensor Networks. IEEE Journal on Selected Areas in Communications 22(6), 1026–1036 (2004)
Kotecha, J.H., Ramachandran, V., Sayeed, A.M.: Distributed Multitarget Classification in Wireless Sensor Networks. IEEE Journal on Selected Areas in Communications 23(4), 703–713 (2005)
Duarte, M.F., Hu, Y.H.: Vehicle Classification in Distributed Sensor Networks. Journal of Parallel and Distributed Computing 64(7), 826–838 (2004)
Flouri, K., Beferull-Lozano, B., Tsakalides, P.: Training A SVM-based Classifier in Distributed Sensor Networks. In: Proc. 14nd European Signal Processing Conference, Florence, Italy, pp. 1–5 (2006)
Shilton, A., Palaniswami, M., Ralph, D., Tsoi, A.C.: Incremental Training of Support Vector Machines. IEEE Trans on Neural Networks 16(1), 114–131 (2005)
Ruping, S.: Incremental Learning with Support Vector Machines. In: Proc. IEEE Int. Conf. on Data Mining, San Jose, CA, USA, pp. 641–642 (2001)
Syed, N., Liu, H., Sung, K.: Incremental Learning with Support Vector Machines. In: Proc. of the 16th Int. Joint Conf. on Artificial Intelligence, Stockholm, pp. 352–356. Morgan Kaufmann, San Francisco (1999)
Domeniconi, C., Gunopoulos, D.: Incremental Support Vector Machine Construction. In: IEEE Int. Conf. on Data Mining, San Jose, CA, USA, pp. 589–592 (2001)
Diehl, C.P., Cauwenberghs, G.: SVM Incremental Learning, Adaptation and Optimization. In: Proc. Int. Joint Conf. on Neural Networks, Portland, OR, pp. 2685–2690 (2003)
Bennett, K., Campbell, C.: Support Vector Machines: Hype or Hallelujah. SIGKDD Explorations, 1–13 (2000)
Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-Time Surveillance of People and Their Activities. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)
Lu, S., Zhang, J., Feng, D.: Classification of Moving Humans Using Eigen-Features and Support Vector Machines. In: Gagalowicz, A., Philips, W. (eds.) CAIP 2005. LNCS, vol. 3691, pp. 522–529. Springer, Heidelberg (2005)
Wang, X., Wang, S., Ma, J.-J.: Dynamic Deployment Optimization in Wireless Sensor Networks. Lecture Notes in Control and Information Sciences, vol. 344, pp. 182–187 (2006)
Wang, X., Jiang, A.-G., Wang, S.: Mobile Agent Based Wireless Sensor Network for Intelligent Maintenance. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3645, pp. 316–325. Springer, Heidelberg (2005)
Rappaport, T.S.: Wireless Communications: Principles and Practice, 2nd edn. Prentice-Hall, Englewood Cliffs, NJ (2002)
Wang, X., Wang, S., Ma, J.-J.: An Improved Particle Filter for Target Tracking in Sensor System. Sensors 7(1), 144–156 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Wang, X., Wang, S., Ma, J. (2007). Collaborative Target Classification for Image Recognition in Wireless Sensor Networks. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-73871-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73870-1
Online ISBN: 978-3-540-73871-8
eBook Packages: Computer ScienceComputer Science (R0)