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Asian Conference on Pattern Recognition

ACPR 2019: Pattern Recognition pp 164–178Cite as

Perceptual Image Anomaly Detection

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12046))

Abstract

We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of “normal” data, aim to detect out-of-distribution (abnormal) samples. Our approach includes a combination of encoder and generator for mapping an image distribution to a predefined latent distribution and vice versa. It leverages Generative Adversarial Networks to learn these data distributions and uses perceptual loss for the detection of image abnormality. To accomplish this goal, we introduce a new similarity metric, which expresses the perceived similarity between images and is robust to changes in image contrast. Secondly, we introduce a novel approach for the selection of weights of a multi-objective loss function (image reconstruction and distribution mapping) in the absence of a validation dataset for hyperparameter tuning. After training, our model measures the abnormality of the input image as the perceptual dissimilarity between it and the closest generated image of the modeled data distribution. The proposed approach is extensively evaluated on several publicly available image benchmarks and achieves state-of-the-art performance.

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Author information

Authors and Affiliations

  1. Philips Research, Moscow, Russia

    Nina Tuluptceva & Irina Fedulova

  2. Philips Research, Eindhoven, The Netherlands

    Bart Bakker

  3. Lomonosov Moscow State University, Moscow, Russia

    Nina Tuluptceva & Anton Konushin

Authors
  1. Nina Tuluptceva
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  2. Bart Bakker
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  3. Irina Fedulova
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  4. Anton Konushin
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Corresponding author

Correspondence to Nina Tuluptceva .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Tuluptceva, N., Bakker, B., Fedulova, I., Konushin, A. (2020). Perceptual Image Anomaly Detection. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_12

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  • DOI: https://doi.org/10.1007/978-3-030-41404-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41403-0

  • Online ISBN: 978-3-030-41404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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