PPforest: Projection Pursuit Classification Forest

#' The image data set 
#'
#' contains  2310 observations of instances from 7 outdoor images
#' \itemize{
#' \item{Type}{ has 7 types of outdoor images, brickface, cement,  foliage, grass, path, sky, and window.}
#' \item{X1}{ the column of the center pixel of the region} 
#' \item{X2}{ the row of the center pixel of the region. }
#' \item{X3}{ the number of pixels in a region = 9. }
#' \item{X4}{ the results of a line extraction algorithm that counts how many lines of length 5 (any orientation) with low contrast, less than or equal to 5, go through the region.}
#' \item{X5}{ measure the contrast of horizontally adjacent pixels in the region. There are 6, the mean and standard deviation are given. This attribute is used as a vertical edge detector.}
#' \item{X6}{ X5 sd}
#' \item{X7}{ measures the contrast of vertically adjacent pixels. Used for horizontal line detection. }
#' \item{X8}{ sd X7}
#' \item{X9}{ the average over the region of (R + G + B)/3}
#' \item{X10}{ the average over the region of the R value.}
#' \item{X11}{ the average over the region of the B value.}
#' \item{X12}{ the average over the region of the G value.}
#' \item{X13}{ measure the excess red: (2R - (G + B))}
#' \item{X14}{ measure the excess blue: (2B - (G + R))}
#' \item{X15}{ measure the excess green: (2G - (R + B))}
#' \item{X16}{ 3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics)}
#' \item{X17}{ mean of X16}
#' \item{X18}{ hue  mean}
#' }
#' @docType data
#' @keywords datasets
#' @name image
#' @usage data(image)
#' @format A data frame contains 2310 observations and 19 variables
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