2018 24th International Conference on Pattern Recognition (ICPR) (2018)
Aug. 20, 2018 to Aug. 24, 2018
Muhammad Jawad , Institute of Management Sciences, Peshawar, Pakistan
Furqan Aziz , Institute of Management Sciences, Peshawar, Pakistan
Edwin Hancock , Department of Computer Science, University of York, YO10 5GH, UK
In this paper we propose a novel approach for defining Local Binary Patterns (LBP) to directly encode graph structure. LBP is a simple and widely used technique for texture analysis in static 2D images, and there is no work in the literature describing its generalisation to graphs. The proposed method (GraphLBP) is efficient and yet effective as a noise-tolerant graph-based representation. We compute the new feature representation for graphs by combining LBP with Galois Fields, using irreducible polynomials. The proposed method is scalable as it preserves the local and global properties of the graph. Experimental results show that GraphLBP can both increase the recognition accuracy and is both simpler and more computationally efficient when compared with state of the art techniques.
Galois fields, Image edge detection, Shape, Pattern recognition, Two dimensional displays, Three-dimensional displays, Task analysis
M. Jawad, F. Aziz and E. Hancock, "Local Binary Patterns for Graph Characterization," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2018, pp. 1241-1246.