基于BoC-BoF特征的图像检索方法研究

Image Retrieval Method Research Based on BoC-BoF Feature

为了优化基于内容的图像检索方法,提出了一种融合特征来表征图像内容。首先,提取基于RootSift描述子的特征词袋(Bag-of-Features,BoF)表示向量,获得图像的边缘和形状信息;其次,采用基于HSV的颜色词袋(Bag-ofColors,BoC)表示向量来代替传统颜色直方图方法,获取图像的颜色信息;最后,将BoF表示向量和BoC表示向量相融合,形成BoC-BoF特征向量。BoC-BoF特征有效地实现了全局特征和局部特征的融合。两个数据集检索的实验结果表明,该方法比其它方法更加有效。

The fusion feature for representing content of images was investigated in order to optimize content-based image retrieval method.Firstly,RootSift-based Bag-of-Features（BoF）were extracted,which capture shape and edge information.Then,Bag-of-Colors（BoC）based on HSV were adopted to replace the traditional color histogram quantization method was adopted,which capture color information.Lastly,BoC-BoF algorithm which integrates BoC vectors and BoF vectors was proposed.BoC-BoF algorithm effectively realizes the integration of global features and local features.The obtained impressive results show that this algorithm is more effective than other methods in two datasets of this paper.