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Relative Stability of Random Projection-Based Image Classification

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10841))

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Abstract

Our aim is to show that randomly generated transformation of high-dimensional data vectors, for example, images, could provide low dimensional features which are stable and suitable for classification tasks. We examine two types of projections: (a) global random projections, i.e., projections of the whole images, and (b) concatenated local projections of spatially-organized parts of an image (for example rectangular image blocks). In both cases, the transformed images provide good features for correct classification. The computational complexity of designing the transformation is linear with respect to the size of images and in case (b) it does not depend on the form of image partition. We have analyzed the stability of classification results with respect to random projection and to different randomly generated training sets. Experiments on the images of ten persons taken from the Extended Yale Database B demonstrate that the methods of classification based on Gaussian random projection are effective and positively comparable with PCA-based methods, both from the point of view of stability and classification accuracy.

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Acknowledgments

This research was supported by grant 041/0145/17 at the Faculty of Electronics, Wrocław University of Science and Technology.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Electronics, Wrocław University of Science and Technology, Wrocław, Poland

    Ewa Skubalska-Rafajłowicz

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  1. Ewa Skubalska-Rafajłowicz
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Correspondence to Ewa Skubalska-Rafajłowicz .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Skubalska-Rafajłowicz, E. (2018). Relative Stability of Random Projection-Based Image Classification. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_65

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  • DOI: https://doi.org/10.1007/978-3-319-91253-0_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

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