基于粗糙集与SVM的图像检索相关反馈算法

通过有效的组织粗糙集理论的约简算法与支持向量集中的分类算法，借助用户的反馈标记，较大的提高了图像检索查全率与查准率，使检索的目标图像更能符合用户的语义特征．由于粗糙集理论的引入消去了本次检索的冗余属性，提高了图像检索的时间复杂性．SVM与相关反馈的结合降低了维数灾难，也降低了高层语义与低层特征的差异带来的困难．

Relevance Feedback Technology of Image Retrieval Based on Rough Set and SVM

Through the effective organization of rough set theory reduction algorithm and the classification of support vector Machine algorithm, with the user＇s feedback marking to improve image retrieval recall and precision, so that the retrieval of the image is more in line with the objectives of the user＇s semantic features. Since the introduction of rough set theory to image retrieve which eliminated the redundant properties and improved the image retrieval time complexity. The combination of SVM and feedback reduced the disaster of dimensions , also reduced the difficulty caused by low--rise features and high--level semantic.