基于小波多尺度分析的彩色图像检索方法

多媒体技术的普及和Internet技术的实施导致了大量图像信息的出现，基于文本关键词的传统检索方法已不能适应图像信息检索的要求，这使得基于内容的图像检索技术逐渐成为目前的研究热点。基于内容检索技术中必不可少的关键步骤就是图像特征的提取，其中可提取的特征有颜色、纹理和形状等。但是，由于图像的每种特征只能抓住图像相似性的某一个方面，因此如何能更好地表示图像就成为基于内容图像检索中一个重要的研究方向。针对该问题，提出了一种基于图像颜色和纹理特征的图像检索方法，其中颜色特征采用HSV颜色空间的直方图，纹理特征采用图像小波多尺度表示方法中细节信息的方差统计量，这样就充分利用了颜色的丰富表现性和小波变换的多分辨性及其变换系数的统计特性。通过对不同类型图像使用不同特征组合进行图像检索查准率的对比实验结果表明，这种图像检索方法是行之有效的。

Color Image Retrieval Method Based on Wavelet Multiresolution Analysis

A large number of images come forth because of the prevalence of multimedia technology and the implement of Internet technology. Since traditional text keyword-based retrieval approach can't adapt to the demand of image data retrieval, so content-based image retrieval(CBIR) technology becomes the current research focus. Among the contend-based retrieval technologies, feature extraction is most important. For instance, color, texture and shape feature etc. But each feature of image can only catch one aspect of the similarity of image, how to represent images better has become a important research field in content-based image retrieval. We come up with a representation method of image based on color and texture feature of image. In this method, we use the histogram of HSV color space as color feature, and the detail information of the multiresolution representation of image as texture feature. Take full advantage of the rich representation of color and the statistics of the wavelet coefficients. Through the comparative experiment on the recall of image retrieval over various image type and various combination of feature, we can conclude that this representation of image is feasible and valid in image retrieval.