基于兴趣区域特征融合的半监督图像检索算法

提出一种融合底层特征、基于兴趣区域的半监督学习图像检索方法,实现了图像内容的语义关联。该方法首先划分图像兴趣区域,提取图像的综合底层特征,然后将其作为训练数据,对图像类别进行半监督学习,建立图像和类别的语义映射,最后分别采用二次式距离和改进的Canberra距离对图像底层特征进行度量,特征空间中图像类的区域中心用正反馈进行迭代更新。通过实验对比,该图像检索算法具有较高的准确率,优于传统的基于内容的图像检索算法。

A semi-supervised image retrieval algorithm based onfeature fusion of the region of interest

A method of image retrieval based on the feature fusion of region of interest was proposed to realize the semantic correlation of images content. First, the regions of interest were divided and the integrated underlying characteristics of image were extracted. Second, the characteristics were used as training data to classify the images by semi supervised learning, then the mapping between images and categories of semantic was established. Finally, the quadratic distance and the improved Canberra distance were respectively used for measuring low level features, and the cluster centers of images in the feature space were updated iteratively through positive feedback. The experiments compared with other algorithms showed that the proposed image retrieval algorithm had higher accuracy and performed more effectively than traditional algorithms.