Abstract
The paper presents application of multinomial logistic regression for color segmentation. The common problem in the subject of image understanding is creation of a large enough corpus for algorithm training. Especially when a large set of classes has to be recognized or if using convolutional neural networks the size and diversity of the training set strongly influences the quality of the resulting system. We present a method of automated generation of training samples by combining a well-known green box technique with multinomial logistic regression for background substitution. We show the encountered problems and their solutions. We present numerous examples of algorithm performance in background substitution. We conclude the paper with presentation of other examples of application of logistic regression for image understanding.
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Notes
- 1.
Authors have GLSL implementation achieving real time speed processing of full HD images on Samsung galaxy S III.
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Acknowledgment
This work was supported by the Polish National Center for Research and Development under the LIDER Grant (No. LIDER/354/L-6/14/NCBR/2015).
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Grega, M. et al. (2017). Application of Logistic Regression for Background Substitution. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2017. Communications in Computer and Information Science, vol 785. Springer, Cham. https://doi.org/10.1007/978-3-319-69911-0_3
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