摘要
传统的基于全局特征的图像检索方法中需要对整幅图像特征提取,计算复杂度大,且容易导致语义歧义.针对这一问题,提出一种基于SURF特征贡献度矩阵的ROI选取和图像检索方法.首先采用SURF算子提取图像局部特征,然后依据特征点的Hessian矩阵计算其贡献度矩阵,并将其应用到ROI检测中;在此基础上,融合并归一化ROI的颜色、纹理以及形状等底层特征,利用非线性高斯距离函数进行相似度匹配,实现图像检索.实验结果表明,与已有算法相比,该算法提取的ROI与人类视觉意图一致性高,检索效果较好.
In traditional image retrieval method, features need to be extracted within the whole region of image, which leads to high computation and semantic ambiguity. To address this issue, this paper proposes a technique to select region of interests（ROI）, and carries out the retrieve process within the ROI. Firstly, SURF feature descriptor is used to extract local features and keypoints. Then, dynamic program is employed to calculate the sum of sub-matrix of feature points distribution, which is finally utilized to extract the ROI. Finally, we integrate the color, texture and shape features into a fused feature within ROI, and use nonlinear Gaussian distance function to retrieve images from the database with user input. Experimental results show that our proposed method has high conformity with human vision, and is effective for image retrieval.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2015年第7期1271-1277,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61202283
61472115)
合肥工业大学春华计划(2013HGCH0019)资助支持
