DocumentCode :
3001274
Title :
Shape of Gaussians as feature descriptors
Author :
Liyu Gong ; Tianjiang Wang ; Fang Liu
Author_Institution :
Intell. & Distrib. Comput. Lab., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear :
2009
fDate :
20-25 June 2009
Firstpage :
2366
Lastpage :
2371
Abstract :
This paper introduces a feature descriptor called shape of Gaussian (SOG), which is based on a general feature descriptor design framework called shape of signal probability density function (SOSPDF). SOSPDF takes the shape of a signal´s probability density function (pdf) as its feature. Under such a view, both histogram and region covariance often used in computer vision are SOSPDF features. Histogram describes SOSPDF by a discrete approximation way. Region covariance describes SOSPDF as an incomplete parameterized multivariate Gaussian distribution. Our proposed SOG descriptor is a full parameterized Gaussian, so it has all the advantages of region covariance and is more effective. Furthermore, we identify that SOGs form a Lie group. Based on Lie group theory, we propose a distance metric for SOG. We test SOG features in tracking problem. Experiments show better tracking results compared with region covariance. Moreover, experiment results indicate that SOG features attempt to harvest more useful information and are less sensitive against noise.
Keywords :
Lie groups; computer vision; probability; shape recognition; Gaussians shapes; Lie group theory; SOSPDF; computer vision; feature descriptors; histogram; parameterized multivariate Gaussian distribution; region covariance; shape of signal probability density function; Computer vision; Covariance matrix; Distance measurement; Extraterrestrial measurements; Gaussian distribution; Gaussian processes; Histograms; Probability density function; Shape; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
ISSN :
1063-6919
Print_ISBN :
978-1-4244-3992-8
Type :
conf
DOI :
10.1109/CVPR.2009.5206506
Filename :
5206506
Link To Document :
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