中文摘要：

传统的基于内容图像检索（CBIR）及跟踪算法主要利用图像的颜色、纹理等特征进行相似性比较,但大量的实验和应用也表明利用颜色和纹理进行图像相似性比较在空间结构和对象形状上难以精确控制,致使图像检索经常出现一些不可预料的结果。为了提高图像在形状、颜色及纹理上的检索精度,提出了一种综合颜色和图像轮廓曲线特征的检索方法。该方法分割图像并提取图像中感兴趣对象的轮廓,对提取的轮廓进行仿射变换及最小值化处理,经处理后的轮廓带有边缘的完整信息,具有几何不变性;利用聚类的颜色信息,提取主聚类的直方图,所提取的直方图不仅包含了主聚类的颜色信息也包含了该聚类的空间位置信息。利用检索对象与被检索对象的颜色距离直方图及轮廓曲线距离偏差的加权平均度量检索及被检索对象的相似性。实验结果表明,针对基于感兴趣对象的图像检索问题,给出了一种具有高度检索精度的算法。

英文摘要：

Traditional Content-Based Image Retrieva（lCBIR） and tracking algorithm mainly uses image color,texture and other features as similarity comparison between two images.However,a large number of experiments and applications also show that it is difficult to precisely control spatial structure and object shape with color and texture for images similarity comparison,and unexpected results are often produced during image retrieving.In order to enhance precision for image retrieval,an image retrieval method containing features of color and object contour curve is presented.Image is segmented and the contour of interested object in image is extracted,and then the contour is transformed by affine and is processed by the minimum.The contour contains the whole information of interested object,and preserves the geometric invariance;with color feature,a histogram for primary cluster is extracted.The histogram extracted contains not only the color information but also space location information for primary cluster.The weighted average for color distance histogram and distance deviation of contour curve is applied as similarity measure between two images.Experiments show that the presented algorithm obtains more robust retrieval precision.