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A similarity-based leaf image retrieval scheme: Joining shape and venation features

机译：基于相似度的叶图像检索方案：结合形状和静脉特征

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摘要

In this paper, we propose a new scheme for similarity-based leaf image retrieval. For the effective measurement of leaf similarity, we have considered shape and venation features together. In the shape domain, we construct a matrix of interest points to model the similarity between two leaf images. In order to improve the retrieval performance, we implemented an adaptive grid-based matching algorithm. Based on the Nearest Neighbor (NN) search scheme, this algorithm computes a minimum weight from the constructed matrix and uses it as similarity degree between two leaf images. This reduces necessary search space for matching. In the venation domain, we construct an adjacency matrix from the intersection and end points of a venation to model similarity between two leaf images. Based on these features, we implemented a prototype mobile leaf image retrieval system and carried out various experiments for a database with 1,032 leaf images. Experimental result shows that our scheme achieves a great performance enhancement compared to other existing methods.
机译：在本文中，我们提出了一种基于相似度的叶片图像检索新方案。为了有效地测量叶片的相似性，我们将形状和脉络特征一起考虑了。在形状域中，我们构造了一个兴趣点矩阵来对两个叶片图像之间的相似性进行建模。为了提高检索性能，我们实现了一种基于网格的自适应匹配算法。该算法基于最近邻（NN）搜索方案，从构造的矩阵中计算出最小权重，并将其用作两个叶子图像之间的相似度。这减少了用于匹配的必要搜索空间。在脉域中，我们根据脉的交点和端点构造一个邻接矩阵，以对两个叶片图像之间的相似性进行建模。基于这些功能，我们实现了原型移动叶子图像检索系统，并对包含1,032个叶子图像的数据库进行了各种实验。实验结果表明，与其他现有方法相比，该方案具有较大的性能增强。

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