Vehicle verification using gabor filter magnitude with gamma distribution modeling

Jing Ming Guo, Heri Prasetyo, Koksheik Wong

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)

Abstract

This letter presents a new method to derive the image feature descriptor for vehicle verification. The effectiveness of the proposed feature descriptor is based on the nature of the Gabor filter magnitude that tends to obey the Gamma distribution. The statistical parameters of the Gabor magnitude are computed using the Maximum Likelihood Estimation (MLE), which is later utilized to construct the feature descriptor. Conventionally, the Gabor magnitude is simply modeled by using Gaussian distribution, and thus the image descriptor consists of mean, standard deviation, and skewness values of the Gabor filter magnitude. However, recent investigations found that the skewness parameter is not contributing towards class separation. Based on our observation, the Gamma distribution provides a better statistical fitting to represent the Gabor filter magnitude when compared to the Gaussian distribution. As documented in the experimental results, the proposed feature descriptor yields higher accuracy for vehicle verification when compared to the conventional schemes.

Original languageEnglish
Pages (from-to)600-604
Number of pages5
JournalIEEE Signal Processing Letters
Volume21
Issue number5
DOIs
Publication statusPublished - May 2014
Externally publishedYes

Keywords

  • Gabor filter
  • Gamma distribution
  • Maximum likelihood estimation
  • Supervised classification
  • Vehicle verification

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