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Image Segmentation Using Structural SVM and Core Vector Machines

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Soft Computing for Problem Solving

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 547))

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Abstract

In this paper, we study the performance of two variants of support vector machines, namely structural support vector machines and core vector machines, on large-scale data. We have used image segmentation as a mechanism to test the classification abilities of these variants on large-scale data. The images are converted to numeric data using various filters, and the labels are generated using the available ground truth image segmentation mask.

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Notes

  1. 1.

    https://www.msri.org/people/members/eranb/.

  2. 2.

    http://chenlab.ece.cornell.edu/projects/touch-coseg/.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology, Mandi, HP, India

    Varuun A. Deshpande & Khriesavinyu Terhuja

Authors
  1. Varuun A. Deshpande
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  2. Khriesavinyu Terhuja
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Corresponding author

Correspondence to Varuun A. Deshpande .

Editor information

Editors and Affiliations

  1. School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Manoj Thakur

  2. School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Samar Agnihotri

  3. School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Bharat Singh Rajpurohit

  4. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Deshpande, V.A., Terhuja, K. (2023). Image Segmentation Using Structural SVM and Core Vector Machines. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_28

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