Texture analysis and classification using shortest paths in graphs

• In this study, we model texture as a non-oriented weighted graph.
• Each pixel is represented as a vertice and an edge connects two vertices if a neighborhood rule is satisfied.
• Then, we explore the shortest paths between pairs of pixels in different scales and orientations of the image.
• A feature vector is built from shortest paths statistics.

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%98.50%,67.30% and 88.00%88.00% of success rate (percentage of samples correctly classified) for Brodatz’s textures, UIUC textures (image size of 200×200200×200 pixels), and original UIUC textures (image size of 640×480640×480 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.