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Identifying Vehicle Exterior Color by Image Processing and Deep Learning

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Advances in Internet, Data & Web Technologies (EIDWT 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 118))

Abstract

The vehicle’s color is one of the factors considered in car purchasing. Hence, color extraction and identification from online vehicle images play an important role in the vehicle e-commerce marketplace. In this paper, we present a vehicle color identification methodology. Image processing techniques are employed to construct feature vectors, which are then used as input to deep neural networks to classify a vehicle’s color into 14 classes. Local relative entropy is utilized as a measure of image segmentation to select the region of interest. Experiments are performed on an image dataset provided by an automobile e-commerce operator. Our implementation results are evaluated and discussed.

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Notes

  1. 1.

    Hue is scaled in the range of 0 to 179 for all the images. Even if the hue is in the range of 0 to 359, two pixels with hue 350 and 10 should have the same distance from a pixel with hue 0.

  2. 2.

    For the last two rows of experiments, the entire vehicle bounding box was gridded into 10 × 10 and 10 × 30.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada

    Somayeh Abniki & Kin Fun Li

  2. VINN Automotive, 844 Courtney Street, Victoria, BC, Canada

    Tom Avant

Authors
  1. Somayeh Abniki
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  2. Kin Fun Li
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  3. Tom Avant
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Corresponding author

Correspondence to Somayeh Abniki .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Information and Computer Engineering, Okayama University of Science, Okayama, Japan

    Elis Kulla

  3. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Makoto Ikeda

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Abniki, S., Li, K.F., Avant, T. (2022). Identifying Vehicle Exterior Color by Image Processing and Deep Learning. In: Barolli, L., Kulla, E., Ikeda, M. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-030-95903-6_46

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