Collaborative Target Classification for Image Recognition in Wireless Sensor Networks

Authors:
Xue Wang;Sheng Wang;Junjie Ma
Affiliations:
State Key Laboratory of Precision Measurement Technology and Instruments, Department, of Precision Instruments, Tsinghua University, Beijing 100084, P.R. China;State Key Laboratory of Precision Measurement Technology and Instruments, Department, of Precision Instruments, Tsinghua University, Beijing 100084, P.R. China;State Key Laboratory of Precision Measurement Technology and Instruments, Department, of Precision Instruments, Tsinghua University, Beijing 100084, P.R. China
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

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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.