【摘要】 由于图像内容特性的复杂性,对图像特征的抽取、索引及检索匹配计算量巨大,空问模型向量动辄几百上千维,使基于内容的图像信息检索在Internet上的应用面临着所谓的"维数灾难"问题。提出了一个新的图像检索模型,该模型融合了颜色的空间分布信息和对象的形状特征。颜色特征采用改进的HSV模型——扇形模型描述;对象的边缘特征采用Canny方法进行计算,并通过Hough变换产生描述对象形状特征(相对位置和方向)的空间信息;图像的相似性比较采用两个全局性向量距离描述。实验结果表明,该模型比采用单一特征(颜色)具有更高的检索效率,是高效率和合理检准率的良好折中。更多还原

【Abstract】 Due to the complexity of image features,computation of characterizing,extracting,indexing,matching for image retrieval is very vast,vector of features is hundreds upon thousands of dimensions at every turn.Applica- tion of CBIR for Intemet faces the difficulty of so-called"dimension calamity".The paper provides a new image re- trieval model which integrates the spacial distributing information of colors and the shape features.The color fea- tures are represented by an amelioration of HSV color model:color sector.To describe the relative position and ori- entation of objects within the image,hue edges in the image,computed using Canny approach,are processed by Hough transform to generate the spatial histogram which is used as shape representation.To address matching of im- age,they define two global distance measures based on the accumulative distance and vector-based distance to com- pare the similarity of two images.Experimental results indicate that the approach provides higher retrieval rate than that of single feature(color),is a better tradeoff between high efficiency and reasonable precision.更多还原