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Algorithm for Image Retrieval Based on Edge Gradient Orientation Statistical Code

机译:基于边缘梯度方向统计码的图像检索算法

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摘要

Image edge gradient direction not only contains important information of the shape, but also has a simple, lower complexity characteristic. Considering that the edge gradient direction histograms and edge direction autocorrelogram do not have the rotation invariance, we put forward the image retrieval algorithm which is based on edge gradient orientation statistical code (hereinafter referred to as EGOSC) by sharing the application of the statistics method in the edge direction of the chain code in eight neighborhoods to the statistics of the edge gradient direction. Firstly, we construct the n-direction vector and make maximal summation restriction on EGOSC to make sure this algorithm is invariable for rotation effectively. Then, we use Euclidean distance of edge gradient direction entropy to measure shape similarity, so that this method is not sensitive to scaling, color, and illumination change. The experimental results and the algorithm analysis demonstrate that the algorithm can be used for content-based image retrieval and has good retrieval results.
机译:图像边缘梯度方向不仅包含形状的重要信息,而且具有简单,低复杂度的特征。考虑到边缘梯度方向直方图和边缘方向自相关图不具有旋转不变性,通过共享统计方法的应用,提出了一种基于边缘梯度方向统计码的图像检索算法(以下简称EGOSC)。链式编码的边缘方向在八个邻域中的相对于边缘梯度方向的统计量。首先,构造n方向向量,并对EGOSC进行最大求和限制,以确保该算法对于旋转有效不变。然后,我们使用边缘梯度方向熵的欧氏距离来测量形状相似度,因此该方法对缩放,颜色和照明变化不敏感。实验结果和算法分析表明,该算法可用于基于内容的图像检索,具有良好的检索效果。

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