利用Krawtchouk矩不变量实现基于形状的图像检索

图像在经过平移、旋转和尺度变化后是否仍具有很好的检索效果是基于形状的图像检索研究的一个难点.本文提出了一种利用Krawtchouk矩不变量实现基于形状的图像检索方法.该方法首先对图像进行灰度变换,然后提取图像的低阶矩,取16个低阶矩不变量作为图像的特征向量,并按照相似性度量输出相似图像从而实现基于形状的图像检索.文中给出了实验结果,并与基于几何矩不变量和基于Zernike矩不变量的图像检索方法进行了比较.结果表明本文的方法具有更好的检索性能,和上述两种方法相比,查全率分别提高了21.52%和7.6%,查准率则分别提高了16.25%和6.25%.

Shape-based Image Retrieval Using Krawtchouk Moment Invariants

One major difficulty in the shape-based image retrieval area is how to keep good results after translation, rotation and scaling of the image.A novel method based on Krawtchouk moment invariants is proposed for shape-based image retrieval.Firstly,the image was transformed into gray image.Then,the low-order moments of the image were exffacted.Finally,16 low-order moment invariants were selected as image feature vectors.The similarity distance measure between feature vectors adopted Euclidean distance function.According to the similarity degree,similar images were retrieved and image retrieval was completed once.Experimental results were given.A comparison was made between the proposed method and the image retrieval method using geometric moment invariants or Zemike moment invariants.The experimental results show that the proposed method is more effective in image retrieval and improves its accuracy Compared with above.mentioned two methods,the recall increases by 21.52% and 7.6%,respectively,while the precision increases by 16.25% and 6.25%,respectively.So the proposed method is more suitable for shape-based image retrieval.