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Novel Object Discovery Using Case-Based Reasoning and Convolutional Neural Networks

  • J. T. Turner
  • Michael W. FloydEmail author
  • Kalyan Moy Gupta
  • David W. Aha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11156)

Abstract

The development of Convolutional Neural Networks (CNNs) has resulted in significant improvements to object classification and detection in image data. One of their primary benefits is that they learn image features rather than relying on hand-crafted features, thereby reducing the amount of knowledge engineering that must be performed. However, another form of knowledge engineering bias exists in how objects are labelled in images, thereby limiting CNNs to classifying the set of object types that have been predefined by a domain expert. We describe a case-based method for detecting novel object types using a combination of an image’s raw pixel values and detectable parts. Our approach works alongside existing CNN architectures, thereby leveraging the state-of-the-art performance of CNNs, and is able to detect novel classes using limited training instances. We evaluate our approach using an existing object detection dataset and provide evidence of our approach’s ability to classify images even if the object in the image has not been previously encountered.

Keywords

Computer vision Novel object discovery Deep learning Convolutional neural networks 

Notes

Acknowledgements

Thanks to the Office of Naval Research for supporting this work.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • J. T. Turner
    • 1
  • Michael W. Floyd
    • 1
    Email author
  • Kalyan Moy Gupta
    • 1
  • David W. Aha
    • 2
  1. 1.Knexus Research CorporationSpringfieldUSA
  2. 2.Navy Center for Applied Research in AINaval Research LaboratoryWashingtonUSA

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