This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Classification by Thresholding
January 1983 (vol. 5 no. 1)
pp. 48-54
Alan H. Feiveson, NASA Johnson Space Center, Houston, TX 77058.
A procedure is given which substantially reduces the processing time needed to perform maximum likelihood classification on large data sets. The given method uses a set of fixed thresholds which, if exceeded by one probability density function, makes it unnecessary to evaluate a competing density function. Proofs are given of the existence and optimality of these thresholds for the class of continuous, unimodal, and quasi-concave density functions (which includes the multivariate normal), and a method for computing the thresholds is provided for the specifilc case of multivariate normal densities. An example with remote sensing data consisting of some 20 000 observations of four-dimensional data from nine ground-cover classes shows that by using thresholds, one could cut the processing time almost in half.
Citation:
Alan H. Feiveson, "Classification by Thresholding," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 1, pp. 48-54, Jan. 1983, doi:10.1109/TPAMI.1983.4767343
Usage of this product signifies your acceptance of the Terms of Use.