DocumentCode :
2668059
Title :
Shared mixture distributions and shared mixture classifiers
Author :
Jarrad, Geog A. ; McMichael, Daniel W.
Author_Institution :
Centre for Sensor Signal & Inf. Process., Mawson Lakes, SA, Australia
fYear :
1999
fDate :
1999
Firstpage :
335
Lastpage :
340
Abstract :
The shared mixture classifier extends the conditional mixture classifier by allowing all the mixture components to contribute to the feature density model. We consider mixtures of elliptically symmetrical densities, and provide gradient ascent and expectation maximisation algorithms for maximum likelihood estimation. Three criteria are examined: the joint, non-discriminative and discriminative likelihoods. The relationships between these criteria are discussed, and we compare the performance of shared and conditional mixture classifiers. Results are presented for an application of a shared mixture classifier to the problem of detecting buried land mines using infrared and visual imagery. They show consistently better performance from the shared model
Keywords :
buried object detection; entropy; gradient methods; image classification; infrared imaging; maximum likelihood estimation; Gaussian mixture model; buried land mine detection; expectation maximisation algorithms; feature density model; gradient ascent method; infrared imagery; maximum likelihood estimation; relative entropy; shared mixture classifiers; shared mixture distributions; visual imagery; Entropy; Information processing; Infrared detectors; Infrared imaging; Integrated circuit modeling; Lakes; Landmine detection; Maximum likelihood estimation; Radial basis function networks; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information, Decision and Control, 1999. IDC 99. Proceedings. 1999
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-5256-4
Type :
conf
DOI :
10.1109/IDC.1999.754179
Filename :
754179
Link To Document :
بازگشت