NDLI: Statistical modeling of the Gabor filter magnitude using Gamma distribution for effectively vehicle verification

Content Provider	IEEE Xplore Digital Library
Author	Jing-Ming Guo Prasetyo, H.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan (Jing-Ming Guo; Prasetyo, H.)
Abstract	Vehicle verification based on still image feature can be considered as supervised classification problem. An image descriptor is directly derived from the Gabor filtered output statistics of a given image. In general, the magnitude of the Gabor filtered output is modeled as the Gaussian distribution. So that the image descriptor is composed from mean, standard deviation, and skewness value of the Gabor filter magnitude [5, 6, 8]. However, Arrospide et. al. [9] argued that the skewness parameter is not meaningful for the class separation. Then, the feature descriptor is well defined only using mean and standard deviation of Gabor output distribution which leads to lower feature dimensionality. Based on our observation, the magnitude of the Gabor filter has strong tendency to follow the Gamma distribution. We propose a new texture descriptor derived from the maximum likelihood estimation of the Gamma distribution for effectively vehicle verification task. Experimental result shows that the proposed method is superior to the former approach under several classifier techniques.
Starting Page	1
Ending Page	4
File Size	247714
Page Count	4
File Format	PDF
e-ISBN	9781479904341
DOI	10.1109/ICICS.2013.6782965
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-10
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Gabor filters Feature extraction Vehicle detection Gaussian distribution Maximum likelihood estimation Convolution vehicle verification Gabor filter Gamma distribution maximum likelihood estimation supervised classification
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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