基于字典统计耦合归一化多重距离的图像检索算法

目的针对当前彩色图像检索技术容易受到色彩的干扰,使其鲁棒性不强等不足,提出一种字典统计耦合归一化多重距离的彩色图像检索算法。方法首先将图像量化并转换成一维信号,然后引入字典统计,将一维信号进行字典编码,并计算编码后的图像多样值,在归一化字典距离的基础上嵌入字典编码图像的多样值,从而定义归一化多重距离(NMD)的相似度量准则,利用NMD对查询图像与数据库图像的多样值进行比较与识别,搜索出与查询图像具有相同特征的最相似图像,完成目标检索。结果在COREL数据库的实验结果表明,相对于当前常用的检索技术,该检索算法具有更高的查准率和查全率,可对彩色图像完成精确检索,有效减低了色彩对检索性能的干扰。结论文中算法具有较好的检索精度,能够较好地用于医疗、商标等领域的目标检索。

The Image Retrieval Algorithm Based on Dictionary Statistics and Normalized Multiple Distance

The work aims to propose an image retrieval algorithm based on dictionary statistics and normalized multiple distance with respect to the disturbance easily caused to the current color image retrieval technology, thus leading to its inadequate robustness and other deficiencies. Firstly, the image was quantized and converted into a one-dimensional signal. Then the dictionary statistics were introduced to code the one-dimensional signals. The diversity value of the image were calculated. At last, on the basis of normalized dictionary distance, the diversity values of image obtained through dictionary coding were inserted and thus the similarity metric criterion of normalized multiple distance (NMD) was defined. NMD was adopted to compare and identify the diversity values of the query image and database image, and search out the image most similar to the query image with the same characteristics, so as to complete the retrieval task. Experimental results from the COREL database showed that: relative to the current commonly used retrieval techniques, the proposed retrieval algorithm had better precision ratio and recall ratio and could accurately retrieve color images, which effectively reduced the disturbance caused to retrieval performance by the color. In conclusion, the proposed algorithm has better retrieval precision and can be used for retrieval tasks in the fields of medical treatment and trademark, etc. in a better manner.