A Q-Convexity Vector Descriptor for Image Analysis
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Shape representation is a main problem in computer vision, and shape descriptors are widely used for image analysis. In this paper, based on the previous work Balázs, P., Brunetti, S.: A New Shape Descriptor Based on a Q-convexity Measure, Lecture Notes in Computers Science 10502, 20th Discrete Geometry for Computer Imagery (DGCI) (2017) 267–278, we design a new convexity vector descriptor derived by the notion of the so-called generalized salient points matrix. We investigate properties of the vector descriptor, such as scale invariance and its behavior in a ranking task. Moreover, we present results on a binary and a multiclass classification problem using k-nearest neighbor, decision tree, and support vector machine methods. Results of these experiments confirm the good behavior of our proposed descriptor in accuracy, and its performance is comparable and, in some cases, superior to some recently published similar methods.
KeywordsShape descriptor Q-convexity Generalized salient point Classification methods
The authors thank P.L. Rosin for providing the dataset used in  and M. Clément for providing the noisy images from DRIVE and CHASEDB1 used in . The collaboration of the authors was supported by the COST Action MP1207 “EXTREMA: Enhanced X-ray Tomographic Reconstruction: Experiment, Modeling, and Algorithms.” The research of Péter Balázs was supported by the NKFIH OTKA [Grant Number K112998].
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