Local self-similarity frequency descriptor for multispectral feature matching

Seungryong Kim, Seungchul Ryu, Bumsub Ham, Junhyung Kim, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

This paper describes a robust feature descriptor called the local self-similarity frequency (LSSF) for the multispectral RGB-NIR feature matching, which uses the frequency response of the local internal layout of self-similarities. A nonlinear relationship between multi-spectral image pairs makes conventional descriptors be sensitive to spectral deformation. To alleviate this problem, the LSSF employs a weighted correlation surface reducing the discrepancy between mul-tispectral images. Furthermore, the LSSF provides a rotation invariance exploiting the frequency response of maximal values on logpolar bins based on the fact that a cyclic shift on the log-polar representation leads only a phase shift in a frequency domain. Experimental results show that LSSF outperforms state-of-the-art descriptors in terms of a recognition rate for multispectral RGB-NIR image pairs.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5746-5750
Number of pages5
ISBN (Electronic)9781479957514
DOIs
Publication statusPublished - 2014 Jan 28

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Kim, S., Ryu, S., Ham, B., Kim, J., & Sohn, K. (2014). Local self-similarity frequency descriptor for multispectral feature matching. In 2014 IEEE International Conference on Image Processing, ICIP 2014 (pp. 5746-5750). [7026162] (2014 IEEE International Conference on Image Processing, ICIP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIP.2014.7026162