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首页> 外文期刊>International journal of image and data fusion >Lightweight target classification for wireless multimedia sensor networks
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Lightweight target classification for wireless multimedia sensor networks

机译：无线多媒体传感器网络的轻量级目标分类

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摘要

Visual target classification is one of the challenging tasks in resource-constrained wireless sensor networks. This article presents binary and multicast animal classification techniques, which use rule-based decision tree, for wireless multimedia sensor networks. In order to reduce the computational complexity on the sensor nodes, the expensive training phase is carried out by a high-power base station. Then, the best IF-THEN rules are extracted from the decision tree classifier and stored in the sensor nodes before being deployed. This would decrease the learning phase time and the energy consumption, while attaining high classification accuracy. Experimental results demonstrated that the proposed classification model can effectively perform visual target classification in wireless multimedia sensor networks.
机译：视觉目标分类是资源受限的无线传感器网络中具有挑战性的任务之一。本文介绍了用于无线多媒体传感器网络的二进制和多播动物分类技术，该技术使用基于规则的决策树。为了降低传感器节点上的计算复杂度，由高功率基站执行昂贵的训练阶段。然后，从决策树分类器中提取最佳IF-THEN规则，并在部署之前将其存储在传感器节点中。这将减少学习阶段时间和能耗，同时获得较高的分类精度。实验结果表明，该分类模型可以有效地对无线多媒体传感器网络中的视觉目标进行分类。

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