The purpose is to classify a given silhouette as one of four types
of vehicle, using a set of features extracted from the
silhouette. The vehicle may be viewed from one of many different
angles. The features were extracted from the silhouettes by the HIPS
(Hierarchical Image Processing System) extension BINATTS, which
extracts a combination of scale independent features utilising both
classical moments based measures such as scaled variance, skewness
and kurtosis about the major/minor axes and heuristic measures such
as hollows, circularity, rectangularity and compactness.
Four "Corgie" model vehicles were used for the experiment: a double
decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400. This
particular combination of vehicles was chosen with the expectation
that the bus, van and either one of the cars would be readily
distinguishable, but it would be more difficult to distinguish
between the cars.
Usage
data(Vehicle)
Format
A data frame with 846 observations on 19 variables, all numerical
and one nominal defining the class of the objects.
[,1]
Comp
Compactness
[,2]
Circ
Circularity
[,3]
D.Circ
Distance Circularity
[,4]
Rad.Ra
Radius ratio
[,5]
Pr.Axis.Ra
pr.axis aspect ratio
[,6]
Max.L.Ra
max.length aspect ratio
[,7]
Scat.Ra
scatter ratio
[,8]
Elong
elongatedness
[,9]
Pr.Axis.Rect
pr.axis rectangularity
[,10]
Max.L.Rect
max.length rectangularity
[,11]
Sc.Var.Maxis
scaled variance along major axis
[,12]
Sc.Var.maxis
scaled variance along minor axis
[,13]
Ra.Gyr
scaled radius of gyration
[,14]
Skew.Maxis
skewness about major axis
[,15]
Skew.maxis
skewness about minor axis
[,16]
Kurt.maxis
kurtosis about minor axis
[,17]
Kurt.Maxis
kurtosis about major axis
[,18]
Holl.Ra
hollows ratio
[,19]
Class
type
Source
Creator: Drs.Pete Mowforth and Barry Shepherd, Turing
Institute, Glasgow, Scotland.
These data have been taken from the UCI Repository Of Machine Learning
Databases at
and were converted to R format by Evgenia Dimitriadou.
References
Turing Institute Research Memorandum TIRM-87-018 "Vehicle
Recognition Using Rule Based Methods" by Siebert,JP (March 1987)
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
UCI Repository of machine learning databases
[http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA:
University of California, Department of Information and Computer
Science.