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Center Symmetric Local Descriptors for Image Classification

Author

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  • Vaasudev Narayanan

    (Indian Institute of Technology (Indian School of Mines), Dhanbad, India)

  • Bhargav Parsi

    (University of California, Los Angeles, USA)

Abstract

Local feature description forms an integral part of texture classification, image recognition, and face recognition. In this paper, the authors propose Center Symmetric Local Ternary Mapped Patterns (CS-LTMP) and eXtended Center Symmetric Local Ternary Mapped Patterns (XCS-LTMP) for local description of images. They combine the strengths of Center Symmetric Local Ternary Pattern (CS-LTP) which uses ternary codes and Center Symmetric Local Mapped Pattern (CS-LMP) which captures the nuances between images to make the CS-LTMP. Similarly, the auhtors combined CS-LTP and eXtended Center Symmetric Local Mapped Pattern (XCS-LMP) to form eXtended Center Symmetric Local Ternary Mapped Pattern (XCS-LTMP). They have conducted their experiments on the CIFAR10 dataset and show that their proposed methods perform significantly better than their direct competitors.

Suggested Citation

  • Vaasudev Narayanan & Bhargav Parsi, 2018. "Center Symmetric Local Descriptors for Image Classification," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 7(4), pages 56-70, October.
    • Handle: RePEc:igg:jncr00:v:7:y:2018:i:4:p:56-70
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