Center Symmetric Local Descriptors for Image Classification

Vaasudev Narayanan and Bhargav Parsi
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Vaasudev Narayanan: Indian Institute of Technology (Indian School of Mines), Dhanbad, India
Bhargav Parsi: University of California, Los Angeles, USA

International Journal of Natural Computing Research (IJNCR), 2018, vol. 7, issue 4, 56-70

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.

Date: 2018
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