基于图像特征细化的海量数据挖掘系统设计与实现

发布时间：2018-08-08 12:22

【摘要】：传统基于图像内容的图像数据挖掘算法,对海量图像特征的分类效率低,对图像数据的挖掘准确率受样本数量影响较大。因此,提出一种基于图像特征细化的海量数据挖掘系统,其中的人机界面可赋予系统较高的交互性。图像搜索引擎能够智能地从互联网海量的图像数据中,采集有价值图像数据和特征。图像预处理模块对图像格式进行变换,完成图像噪声因素的过滤等操作,并对采集图像特征进行细化。数据挖掘模块依据采集的图像特征细化结果塑造CMQL语句,从图像数据库中挖掘出有价值的图像数据。系统实现部分给出了数据挖掘查询语言CMQL进行图像数据的挖掘过程。实验结果表明,所设计系统具有较高的查准率和查全率。
[Abstract]:The traditional image data mining algorithm based on image content has low efficiency for the classification of massive image features, and the accuracy of the mining of image data is affected by the number of samples. Therefore, a mass data mining system based on image feature refinement is proposed. The human-computer interface can give the system high interaction. It can collect valuable image data and features intelligently from the massive image data of the Internet. The image preprocessing module transforms the image format, completes the filtering of the image noise factors, and refines the feature of the image acquisition. The data mining module builds the CMQL statement according to the image feature refinement result. In the system realization part, the data mining query language CMQL is used to excavate the image data. The experimental results show that the designed system has high precision and recall.
【作者单位】：福建教育学院;浙江理工大学;
【基金】：国家自然科学基金(50875245)
【分类号】：TP391.41;TP311.13

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