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Shape recognition is the main challenging problem in computer vision.Different approaches and tools are used to solve this problem. Most existingapproaches to object recognition are based on pixels. Pixel-based methods aredependent on the geometry and nature of the pixels, so the destruction ofpixels reduces their performance. In this paper, we study the ability of graphsas shape recognition. We construct a graph that captures the topological andgeometrical properties of the object. Then, using the coordinate and relationof its vertices, we extract features that are robust to noise, rotation, scalevariation, and articulation. To evaluate our method, we provide differentcomparisons with state-of-the-art results on various known benchmarks,including Kimia's, Tari56, Tetrapod, and Articulated dataset. We provide ananalysis of our method against different variations. The results confirm ourperformance, especially against noise.

New Graph-based Features For Shape Recognition