Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks

We focus on the issue of ground-based cloud classification in wireless sensor networks (WSN) and propose a novel feature learning algorithm named discriminative salient local binary pattern (DSLBP) to tackle this issue. The proposed method is a two-layer model for learning discriminative patterns. T...

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Main Authors: Shuang Liu, Zhong Zhang
Format: Article
Language:English
Published: Wiley 2015-10-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2015/327290
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author Shuang Liu
Zhong Zhang
author_facet Shuang Liu
Zhong Zhang
author_sort Shuang Liu
collection DOAJ
description We focus on the issue of ground-based cloud classification in wireless sensor networks (WSN) and propose a novel feature learning algorithm named discriminative salient local binary pattern (DSLBP) to tackle this issue. The proposed method is a two-layer model for learning discriminative patterns. The first layer is designed to learn the most salient and robust patterns from each class, and the second layer is used to obtain features with discriminative power and representation capability. Based on this strategy, discriminative patterns are obtained according to the characteristics of training cloud data from different sensor nodes, which can adapt variant cloud images. The experimental results show that the proposed algorithm achieves better results than other state-of-the-art cloud classification algorithms in WSN.
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institution Kabale University
issn 1550-1477
language English
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record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-a48e1312583a4e0a9218dea24da448012025-02-03T05:44:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/327290327290Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor NetworksShuang LiuZhong ZhangWe focus on the issue of ground-based cloud classification in wireless sensor networks (WSN) and propose a novel feature learning algorithm named discriminative salient local binary pattern (DSLBP) to tackle this issue. The proposed method is a two-layer model for learning discriminative patterns. The first layer is designed to learn the most salient and robust patterns from each class, and the second layer is used to obtain features with discriminative power and representation capability. Based on this strategy, discriminative patterns are obtained according to the characteristics of training cloud data from different sensor nodes, which can adapt variant cloud images. The experimental results show that the proposed algorithm achieves better results than other state-of-the-art cloud classification algorithms in WSN.https://doi.org/10.1155/2015/327290
spellingShingle Shuang Liu
Zhong Zhang
Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks
International Journal of Distributed Sensor Networks
title Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks
title_full Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks
title_fullStr Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks
title_full_unstemmed Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks
title_short Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks
title_sort learning discriminative salient lbp for cloud classification in wireless sensor networks
url https://doi.org/10.1155/2015/327290
work_keys_str_mv AT shuangliu learningdiscriminativesalientlbpforcloudclassificationinwirelesssensornetworks
AT zhongzhang learningdiscriminativesalientlbpforcloudclassificationinwirelesssensornetworks