【摘要】 传统的基于内容图像检索技术对图像领域没有限制,而宽泛的图像领域不仅严重影响了系统检索精度,而且增加了系统查询时间.本文提出一种基于SVR(Support Vector Regression)分类的多特征彩色图像检索新算法,该算法首先提取出图像的颜色、空间和纹理信息并作为图像的特征向量,然后以回归型支持向量机(SVR)为学习机器,对图像库进行分类处理以缩小图像领域范围,最后在较小的领域范围内进行图像检索.实验结果表明,本文算法能够准确和高效地查找出用户所需内容的彩色图像,并且具有较好的查准率和查全率.更多还原

【Abstract】 In the traditional approach of content-based image retrieval,the wide image domain results in the wide semantic gap between the low-level features and the high-level concepts.In this paper,a content-based color image retrieval method using SVR(Support Vector Regression)classification is proposed.Firstly,the color feature,spatial feature and texture feature are extracted from the color image as the eigenvectors.Then,the SVR are trained for natural image classification.Finally,the resulting image class model is incorporated into image retrieval system,so that the users can search natural image by classes.Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images.更多还原