Abstract
While most existing image recognition benchmarks consist of relatively high quality data, in the practical applications images can be affected by various types of distortions. In this paper we experimentally evaluate the extent to which image distortions affect classification based on HOG feature descriptors. In an experimental study based on several benchmark datasets and classification algorithms we evaluate the impact of Gaussian, quantization and salt-and-pepper noise. We examine both known and random types of distortion, and evaluate the possibility of applying distortions on training data and using denoising to mitigate the negative impact of distortions. Although presence of distortions significantly impede classification with the HOG features, in the paper we show how this negative effect can be greatly mitigated in practical realizations. Experimental results underpin our findings.
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Acknowledgment
This work was supported by the Polish National Science Center under the grant no. 2014/15/B/ST6/00609 and the PLGrid infrastructure.
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Bukała, A., Koziarski, M., Cyganek, B., Koc̨, O.N., Kara, A. (2020). The Impact of Distortions on the Image Recognition with Histograms of Oriented Gradients. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications. IP&C 2019. Advances in Intelligent Systems and Computing, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-31254-1_21
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DOI: https://doi.org/10.1007/978-3-030-31254-1_21
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