Texture analysis and classification using shortest paths in graphs
Texture analysis and classification using shortest paths in graphs
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DOI：
10.1016/j.patrec.2013.04.013
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Jarbas Joaci de Mesquita Sá Junior;A. Backes;P. C. Cortez
中科院分区：
文献类型：
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作者：
Jarbas Joaci de Mesquita Sá Junior;A. Backes;P. C. Cortez
纹理是计算机视觉领域中一个非常重要的属性。提出了一种基于图论的纹理分析方法。基本上，我们将图像的像素转换为无向加权图的顶点，并探索图像中不同尺度和方向的像素对之间的最短路径。将该方法应用于Brodatz纹理和UIUC纹理数据集，以评估其区分不同类型纹理的能力。对Brodatz纹理、UIUC纹理（图像大小为200×200像素）和原始UIUC纹理（图像大小为640×480像素），使用该方法的标准参数，最佳分类结果分别为98.50%、67.30%和88.00%。这些结果证明，所提出的方法是一个有效的工具，纹理分析，一旦他们是上级的结果所取得的传统和新的纹理描述符在文献中。
Texture is a very important attribute in the field of computer vision. This work proposes a novel texture analysis method which is based on graph theory. Basically, we convert the pixels of an image into vertices of an undirected weighted graph and explore the shortest paths between pairs of pixels in different scales and orientations of the image. This procedure is applied to Brodatz’s textures and UIUC texture dataset in order to evaluate its capacity of discriminating different kinds of textures. The best classification results using the standard parameters of the method are 98.50%,67.30% and 88.00% of success rate (percentage of samples correctly classified) for Brodatz’s textures, UIUC textures (image size of 200×200 pixels), and original UIUC textures (image size of 640×480 pixels), respectively. These results prove that the proposed approach is an efficient tool for texture analysis, once they are superior to the results achieved by traditional and novel texture descriptors presented in literature.