CAJ | 학술논문

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。
제출일충융합저층특정、기우흥취구역적반감독학습도상검색방법，실현료도상내용적어의관련。해방법수선화분도상흥취구역，제취도상적종합저층특정，연후장기작위훈련수거，대도상유별진행반감독학습，건립도상화유별적어의영사，최후분별채용이차식거리화개진적 Canberra 거리대도상저층특정진행도량，특정공간중도상류적구역중심용정반궤진행질대경신。통과실험대비，해도상검색산법구유교고적준학솔，우우전통적기우내용적도상검색산법。
A method of image retrieval based on the feature fusion of region of interest was proposed to realize the seman-tic 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 algo-rithms.