区域图像检索(RBIR)是基于内容图像检索(CBIR)的一个分支,它以图像分割为基础,通过图像局部视觉特征的相似性进行图像检索。由于准确的图像分割技术尚不成熟,区域图像检索性能容易受到冗余分割和错误分割的影响。为了降低RBIR中图像分割的影响,提出了一种基于前景和背景划分的区域图像检索方法。该方法通过规则分块、图像分类和有效区域定位来得到图像分割区域,然后应用中心对象提取算法(COEA)获得图像主体对象,最后提取颜色和纹理特征进行相似度匹配。实现了一个基于上述方法的RBIR系统ObFind,实验结果表明该方法不仅具有与SIMPLIcity相当的检索性能,而且计算复杂度更低。

Region-based Image Retrieval(RBIR) is a sub-branch of Contentbased Image Retrieval(CBIR).It employs image segmentation to extract local visual feature and retrieves images by similarity matching.However,as precise image segmentation is still immature,the performance of RBIR systems is subject to redundant and inaccurate segmentation.In order to reduce adverse effect of image segmentation in RBIR,a new method based on partition of foreground and background is proposed.In the method,image segmentation regions are obtained by applying regular block,classification and valid region location.And the principal object is extracted using the Central Object Extraction Algorithm(COEA).Then images are retrieved by similarity matching based on extracted color and texture feature.In the paper,a RBIR system named ObFind is implemented according to the proposed method.The experimental results show that the proposed method not only has comparable performance to SIMPLIcity but also reduces computation complexity.