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A complex network-based approach for boundary shape analysis. (English) Zbl 1162.68594
Summary: This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has an efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, curvature, Zernike moments and multiscale fractal dimension).

MSC:
68T10 Pattern recognition, speech recognition
68U10 Computing methodologies for image processing
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