lrfclass(1)

PROGRAM
lrfclass - Classify an image using the Localized Receptive Field classifier.

SYNTAX

lrfclass

-i1 input image

-i2 cluster center image

-i3 cluster variance image

-i4 weight image

-o  classified image

[-b]
    Border Width

[-V]
    Gives the version for lrfclass

[-U]
    Gives the usage for lrfclass

[-P]
    Prompts for command line options

[-A [file1]]
    Creates the answer file called lrfclass.ans or file1

[-a [file1]]
    Uses lrfclass.ans or file1 as the answer file

DESCRIPTION
lrfclass classifies an image using the Localized Receptive Field classifier (LRF). The Localized Receptive Field (LRF) is based on a single layer of self-organizing, "localized receptive field" units, followed by a single layer perceptron. The single layer of perceptron units use the LMS or Adaline learning rule to adjust the weights. The weights are adjusted or "trained" using the companion program, "lrftrain". After training the weights, using the "lrftrain" program, a number of similar images may be quickly classified with this program based on the training data set.

LRF network theory

The basic network model of the LRF consists of a two layer topology. The first layer of "receptive field" nodes are trained using a clustering algorithm, such as K-means, or some other algorithm which can determine the receptive field centers. Each node in the first layer computes a receptive field response function, which should approach zero as the distance from the center of the receptive field is increased. The second layer of the LRF model sums the weighted outputs of the first layer, which produces the output or response of the network. A supervised LMS rule is used to train the weights of the second layer nodes.

The response function of the LRF network is formulated as follows:
             f(x) = SUM(Ti * Ri(x)) where,
      Ri(x) = Q( ||x - xi|| / Wi )
      x - is a real valued vector in the input space,
      Ri - is the ith receptive field response function,
      Q - is a radially symmetric functio with a single
            maximum at the origin, decreasing to zero at
            large radii,
      xi - is the center of the ith receptive field,
      Wi - is the width of the ith receptive field,
      Ti - is the weight associated with each receptive field.

The receptive field response functions ( Ri(x) ), should be formulated such that they decrease rapidly with increasing radii. This ensures that the response functions provide highly localized representations of the input space. The response function used in this algorithm is modeled after the gaussian, and uses the trace of the covariance matrix to set the widths of the receptive field centers.

Prior to using this algorithm, it is necessary to "train" the weights for the output layer by running the companion program, "lrftrain", on a previously clustered image. Thus the inputs to this program are the original input image (-i1), the cluster center image (-i2), the cluster variance image (-i3), and the weight image (-i4). The original input image contains all of the features used in the original clustering (ie. vkmeans). The cluster center image (-i2) contains the locations of the cluster centers in the input feature space, which fixes the centers of the localized receptive fields. The cluster variance image (-i3) contains the variances associated with each cluster center. This establishes the widths of the localized receptive field gaussians. The weight image (-i4) contains all of the weights for each node in the output layer.

The number of receptive field response nodes in the first layer of the LRF is determined by the number of cluster centers in the "cluster center" image. The number of output classes, and hence the number of output nodes in the second (ie. last) layer, is determined by the number of desired classes that was specified in the "supervised" classification phase of the clustering. This information is contained in the last band of the cluster center image. The number of weights in the network is determined by the number of receptive field response nodes and the number of output nodes. That is,
  #Wts = (#rf_response_nodes * #output_nodes) + #output_nodes

The resulting output image is classified with the desired number of classes specified in the last band of the "cluster center" (-i2) image. The number of desired classes corresponds to the number of output nodes in the last layer of the LRF network. This classified image is of data storage type INTEGER.

Input Options

-b  is an integer that specifies the border width, in pixels, encompassing the desired region of the image to be classified. This region is ignored during the classification process.

This routine was written with the help of and ideas from Dr. Don Hush, University of New Mexico, Dept. of EECE.

REQUIRED ARGUMENTS

-i1 type: infile
    desc: input image

-i2 type: infile
    desc: cluster center image

-i3 type: infile
    desc: cluster variance image

-i4 type: infile
    desc: weight image

-o  type: outfile
    desc: classified image

OPTIONAL ARGUMENTS

-b  type: integer
    desc: Border Width
    default: 0
    bounds: 0 < [-b] < 100

EXAMPLES
lrfclass -i1 feature_image.xv -i2 cluster_centers -i3 variances -i4 weight_image -o classified_image -b 4

This example uses feature_image.xv as the input feature image, and the three other images from the companion program "lrftrain". These include the cluster_centers image, the variances image, and the weight_image. The resulting classified image is stored as "classified_image". A border width of 4 pixels is specified, which will cause the outermost four pixels of the image to be ignored.

SEE ALSO
intro(1), verror(3), vgparms(3), vutils(3), llrfclass(3) lrftrain(1)

RESTRICTIONS
All input images MUST be of data storage type FLOAT. The resulting classified image (-o) is of data storage type INTEGER.

COPYRIGHT
Copyright 1991, University of New Mexico. All rights reserved.