基于BOF图像检索算法的变电站设备图像分类  

Image Classification of Substation Equipment Based on BOF Image Retrieval Algorithm

作　　者：赵庆生 王雨滢 梁定康 郭尊[2] Zhao Qingsheng;Wang Yuying;Liang Dingkang;Guo Zun(Shanari Key Laboratory of Poucer System Operation and Coutrol,College of Electrical and Pouwer Engineering,Taiuan University of Technology.Taiyuan,Shanxi 030024,China;School of Electrical and Electrowic Einginering,North China Electric Pouer University,Beijing 102206,China)

机构地区：[1]太原理工大学电气与动力工程学院电力系统运行与控制山西省重点实验室,山西太原030024 [2]华北电力大学电气与电子工程学院,北京102206

出　　处：《激光与光电子学进展》2020年第18期130-138,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金青年科学基金(51907138);山西省自然科学基金(201801D221362)。

摘　　要：基于BOF(Bag of features)图像检索算法对电气设备图像进行分类,首先,通过加速鲁棒特征(SURF)算法寻找特征点位置,构造高维特征描述算子对特征进行描述和统计。然后,利用K-means聚类算法处理特征描述算子,得到独立的视觉词汇并汇总为特定数目的码书。将码书中的特征描述算子进行量化和加权统计,用特征向量直方图表示整个图像。最后,用训练集图像的高维特征向量进行机器学习,对未知图像进行快速准确分类。将自然光条件下拍摄的电气设备图像和电气设备工作状态下的红外图像作为两个实验样本集进行分类测试,结果表明,该算法可对不同图像集实现快速准确分类,准确率可达95.59%。This paper proposes a BOF(bag of features image)retrieval algorithm to classify electrical equipment images.First,the location of feature points is determined by speed up robust features(SURF)algorithm,and a high-dimensional feature description operator is constructed to describe and count the features.Then,the K-means clustering algorithm is used to deal with the feature description operators,and the independent visual vocabularies are collected into a specific number of codebooks.The feature description operators in codebooks are quantified and weighted,and the eigenvector histogram is used to represent the entire image.Finally,the high-dimensional feature vectors of the training set images are used for machine learning,and the unknown images are classified quickly and accurately.Electrical equipment images under natural light conditions and infrared images under the working conditions of electrical equipment are taken as two experimental sample sets for classification test.The results show that the algorithm can classify different image sets quickly and accurately with the highest accuracy of 95.59%.

关 键 词：机器视觉 图像分类 图像检索 特征量化 特征聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]