基于改进的Bag of Visual Words算法的SAR图像目标分类

基于改进的Bag of Visual Words算法的SAR图像目标分
类
王跃;薄华
【期刊名称】《电子设计工程》
【年(卷),期】2013(21)12
【摘要】“视觉词袋”(Bag of Visual Words,BOV)算法是一种有效的基于语义特征表达的物体识别算法.针对传统BOV模型存在的不足,综合利用SAR图像的灰度和纹理特征,提出基于感兴趣目标(Target of Interest,TOI)的“视觉词袋”算法.首先,对训练图像进行TOI选取,用灰度共生矩阵模型提取TOI的纹理特征,再结合灰度特征,组成多维特征向量集,以簇内相似度最高、数据分布密度最大为准则,生成“视觉词袋”.其次,对测试图像,依据已生成的“视觉词袋”,采用支持向量机(Support Vector Machine,SVM)分类器,实现SAR图像感兴趣目标的有效分类.实验结果表明,与传统的“视觉词袋”构建算法相比,该算法在分类正确率提高的同时,能够在训练图像较少的情况下达到良好的分类效果.%Bag of Visual Words (BOV) algorithm is an efficient object recognition algorithm based on semantic features representation,which derives from text-based search algorithm based on semantic.To optimize the construction of the traditional Bag of Visual Words,this paper,comprehensively utilizing the model of gray and texture information,presents an optimized algorithm for the construction of Bag of Visual Words based on Target of Interest (TOI).Firstly,the TOI is extracted from training images.Gray level co-occurrence matrix (GLCM) is introduced to the extraction of texture features that produce a multi-
dimensional feature vectors set with gray information.With the highest similarity inside clusters and the maximum data density taken as the cluster criterion,the cluster centers are ly,Bag of Visual Words is constructed.Secondly,according to the produced BOV,using Support Vector Machine (SVM) Classifier realizes object classification of SAR image.Experimental results show the algorithm proposed in the paper can obtain high classification accuracy in the case of a few training images comparing with the traditional BOV.
【总页数】5页(P124-127,131)
【作者】王跃;薄华
【作者单位】上海海事大学信息工程学院,上海201306;上海海事大学信息工程学院,上海201306
【正文语种】中文
【中图分类】TP391
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