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RNCE: A New Image Segmentation Approach

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

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Abstract

Semantic image segmentation based on deep learning is gaining popularity because it is giving promising results in medical image analysis, automated land categorization, remote sensing, and other computer vision applications. Many algorithms have been designed in recent years, yet there is scope for further improvement in computer vision research. We have proposed a unique ensemble method called Ranking and Nonhierarchical Comparison Ensemble (RNCE) for semantic segmentation of landcover images based on the Ranking and Nonhierarchical Comparison methodology. Our approach has been tested on pretrained models showing improved accuracy and mean IoU with respect to the existing method. The code is available at: https://github.com/vekash2021/RNCE.git.

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Acknowledgements

This work has been carried out in the Digital Control and Image Processing Lab, ETCE Department, Jadavpur University.

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Authors and Affiliations

  1. Electronics and Telecommunication Engineering, Jadavpur, Kolkata, West Bengal, 700032, India

    Vikash Kumar, Asfak Ali & Sheli Sinha Chaudhuri

Authors
  1. Vikash Kumar
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  2. Asfak Ali
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  3. Sheli Sinha Chaudhuri
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Corresponding author

Correspondence to Vikash Kumar .

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Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Kumar, V., Ali, A., Chaudhuri, S.S. (2023). RNCE: A New Image Segmentation Approach. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_44

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