移动群智感知中图片情境信息的聚类动态查找算法

A Dynamic Search Algorithm of Picture Context Information Clustering in Mobile Crowdsensing

使用移动设备摄像头进行感知是移动群智感知主要形式之一,预先利用照片的情境信息聚类可以减少图片特征相似计算,提高照片冗余判断效率。为了提高情境信息聚类精度,本文提出一种聚类动态查找算法,解决动态聚类近边缘相似的问题。首先,根据PTree聚类算法是否聚类到已有区间分为实枝叶和虚枝叶,实枝叶的数据直接上传,虚枝叶进一步动态查找最佳相似匹配区间;然后,基于使用局部扩大再动态缩小的思想,减少动态聚类数据点之间平均距离;最后,聚类到同一区间的图片集进行相似过滤。通过设计的APP收集带有情境信息的照片数据,结果表明,与现有方案相比,在保证覆盖度的前提下有效减少所需上传照片数量,提高去冗余效果。

Mobile device camera sensing is one of the main forms of mobile crowdsensing.The context information clustering of photos in advance can reduce the similarity calculation of image features and improve the efficiency of redundant judgment of photos.In order to improve the accuracy of context information clustering,this paper proposes a clustering dynamic search algorithm,which solves the problem of dynamic clustering near edge similarity.Firstly,according to whether the PTree clustering algorithm clusters to the existing interval,it is divided into real branches and virtual branches.The data of real branches and leaves are uploaded directly,and the virtual branches and leaves are further dynamically searched for the best similarity matching interval;then,based on the idea of using local expansion and then dynamic reduction,the average distance between dynamic clustering data points is reduced;finally,the redundant photos are removed by clustering to the same interval image set.By collecting photo data with context information through the designed APP,the results show that,compared with the existing schemes,the number of photos to be uploaded can be reduced and the effect of redundancy can be improved.