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Data augmentation based on shape space exploration for low-size datasets: application to 2D shape classification

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Abstract

This article introduces a novel 2D shape data augmentation approach based on intra-class shape space exploration. The proposed method relies on a geodesic interpolation between shapes, leveraging invariant-based morphing techniques. By blending a 2D shape pair belonging to a given class, we are able to generate nonlinear augmentations, hence covering more variations within the shape space. In particular, we formulate data augmentation as an optimization problem that minimizes the deformations between two shapes using the Generalized Finite Fourier Invariant Descriptor. The proposed augmentation technique is evaluated using numerous Convolution Neural Network architectures for 2D shape classification. The results indicate the superiority of the proposed method as compared to state-of-the-art techniques when considering small-scale datasets.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. CRISTAL Laboratory GRIFT Group, National School of Computer Science, Manouba University, 2010, Manouba, Tunisia

    Emna Ghorbel & Faouzi Ghorbel

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  1. Emna Ghorbel
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  2. Faouzi Ghorbel
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Correspondence to Emna Ghorbel.

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Ghorbel, E., Ghorbel, F. Data augmentation based on shape space exploration for low-size datasets: application to 2D shape classification. Neural Comput & Applic 36, 10031–10054 (2024). https://doi.org/10.1007/s00521-024-09798-5

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