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ColorNet: Investigating the Importance of Color Spaces for Image Classification

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11364))

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Abstract

Image classification is a fundamental application in computer vision. Recently, deeper networks and highly connected networks have shown state of the art performance for image classification tasks. Most datasets these days consist of a finite number of color images. These color images are taken as input in the form of RGB images and classification is done without modifying them. We explore the importance of color spaces and show that color spaces (essentially transformations of original RGB images) can significantly affect classification accuracy. Further, we show that certain classes of images are better represented in particular color spaces and for a dataset with a highly varying number of classes such as CIFAR and Imagenet, using a model that considers multiple color spaces within the same model gives excellent levels of accuracy. Also, we show that such a model, where the input is preprocessed into multiple color spaces simultaneously, needs far fewer parameters to obtain high accuracy for classification. For example, our model with 1.75M parameters significantly outperforms DenseNet 100-12 that has 12M parameters and gives results comparable to Densenet-BC-190-40 that has 25.6M parameters for classification of four competitive image classification datasets namely: CIFAR-10, CIFAR-100, SVHN and Imagenet. Our model essentially takes an RGB image as input, simultaneously converts the image into 7 different color spaces and uses these as inputs to individual densenets. We use small and wide densenets to reduce computation overhead and number of hyperparameters required. We obtain significant improvement on current state of the art results on these datasets as well.

Supported by NSFC project Grant No. U1833101, Shenzhen Science and Technologies project under Grant No. JCYJ20160428182137473 and the Joint Research Center of Tencent and Tsinghua.

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Authors and Affiliations

  1. Computer Science Department, Tsinghua University, Beijing, 10084, China

    Shreyank N. Gowda

  2. Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China

    Chun Yuan

Authors
  1. Shreyank N. Gowda
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  2. Chun Yuan
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Correspondence to Shreyank N. Gowda .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Gowda, S.N., Yuan, C. (2019). ColorNet: Investigating the Importance of Color Spaces for Image Classification. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_36

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  • DOI: https://doi.org/10.1007/978-3-030-20870-7_36

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