vehicle.arff

是UCI数据库中的一些有代表性的数据集

% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!IMPORTANT!!!!!!!!!!!!!!!!!!!!!!!!!!!!
% 
%         This dataset comes from the Turing Institute, Glasgow, Scotland.
%         If you use this dataset in any publication you must acknowledge this
%         source.
% 
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
% 
% NAME
%         vehicle silhouettes
% 
% PURPOSE
%         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.  
% 
% PROBLEM TYPE
%         classification
%         
% SOURCE
%         Drs.Pete Mowforth and Barry Shepherd
%         Turing Institute
%         George House
%         36 North Hanover St.
%         Glasgow
%         G1 2AD
% 
% CONTACT
%         Alistair Sutherland
%         Statistics Dept.
%         Strathclyde University
%         Livingstone Tower
%         26 Richmond St.
%         GLASGOW G1 1XH
%         Great Britain
%         
%         Tel: [phone] x3033
%         
%         Fax: [phone] 
%         
%         e-mail: [e-mail]
% 
% HISTORY
%         This data was originally gathered at the TI in 1986-87 by
%         JP Siebert. It was partially financed by Barr and Stroud Ltd.
%         The original purpose was to find a method of distinguishing
%         3D objects within a 2D image by application of an ensemble of
%         shape feature extractors to the 2D silhouettes of the objects.
%         Measures of shape features extracted from example silhouettes
%         of objects to be discriminated were used to generate a class-
%         ification rule tree by means of computer induction.
%          This object recognition strategy was successfully used to 
%         discriminate between silhouettes of model cars, vans and buses
%         viewed from constrained elevation but all angles of rotation.
%          The rule tree classification performance compared favourably
%         to MDC (Minimum Distance Classifier) and k-NN (k-Nearest Neigh-
%         bour) statistical classifiers in terms of both error rate and
%         computational efficiency. An investigation of these rule trees
%         generated by example indicated that the tree structure was 
%         heavily influenced by the orientation of the objects, and grouped
%         similar object views into single decisions.
% 
% DESCRIPTION
%          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.
%          The images were acquired by a camera looking downwards at the
%         model vehicle from a fixed angle of elevation (34.2 degrees
%         to the horizontal). The vehicles were placed on a diffuse
%         backlit surface (lightbox). The vehicles were painted matte black
%         to minimise highlights. The images were captured using a CRS4000
%         framestore connected to a vax 750. All images were captured with
%         a spatial resolution of 128x128 pixels quantised to 64 greylevels.
%         These images were thresholded to produce binary vehicle silhouettes,
%         negated (to comply with the processing requirements of BINATTS) and
%         thereafter subjected to shrink-expand-expand-shrink HIPS modules to
%         remove "salt and pepper" image noise.
%          The vehicles were rotated and their angle of orientation was measured
%         using a radial graticule beneath the vehicle. 0 and 180 degrees
%         corresponded to "head on" and "rear" views respectively while 90 and
%         270 corresponded to profiles in opposite directions. Two sets of
%         60 images, each set covering a full 360 degree rotation, were captured
%         for each vehicle. The vehicle was rotated by a fixed angle between 
%         images. These datasets are known as e2 and e3 respectively.
%          A further two sets of images, e4 and e5, were captured with the camera 
%         at elevations of 37.5 degs and 30.8 degs respectively. These sets
%         also contain 60 images per vehicle apart from e4.van which contains
%         only 46 owing to the difficulty of containing the van in the image
%         at some orientations.
% 
% ATTRIBUTES
%         
%         COMPACTNESS     (average perim)**2/area
%         
%         CIRCULARITY     (average radius)**2/area
%         
%         DISTANCE CIRCULARITY    area/(av.distance from border)**2
%         
%         RADIUS RATIO    (max.rad-min.rad)/av.radius
%         
%         PR.AXIS ASPECT RATIO    (minor axis)/(major axis)
%         
%         MAX.LENGTH ASPECT RATIO (length perp. max length)/(max length)
%         
%         SCATTER RATIO   (inertia about minor axis)/(inertia about major axis)
%         
%         ELONGATEDNESS           area/(shrink width)**2
%         
%         PR.AXIS RECTANGULARITY  area/(pr.axis length*pr.axis width)
%         
%         MAX.LENGTH RECTANGULARITY area/(max.length*length perp. to this)
%         
%         SCALED VARIANCE         (2nd order moment about minor axis)/area
%         ALONG MAJOR AXIS
%         
%         SCALED VARIANCE         (2nd order moment about major axis)/area
%         ALONG MINOR AXIS 
%         
%         SCALED RADIUS OF GYRATION       (mavar+mivar)/area
%         
%         SKEWNESS ABOUT  (3rd order moment about major axis)/sigma_min**3
%         MAJOR AXIS
%         
%         SKEWNESS ABOUT  (3rd order moment about minor axis)/sigma_maj**3
%         MINOR AXIS
%                 
%         KURTOSIS ABOUT  (4th order moment about major axis)/sigma_min**4
%         MINOR AXIS  
%                 
%         KURTOSIS ABOUT  (4th order moment about minor axis)/sigma_maj**4
%         MAJOR AXIS
%         
%         HOLLOWS RATIO   (area of hollows)/(area of bounding polygon)
%         
%          Where sigma_maj**2 is the variance along the major axis and
%         sigma_min**2 is the variance along the minor axis, and
%         
%         area of hollows= area of bounding poly-area of object 
%         
%          The area of the bounding polygon is found as a side result of
%         the computation to find the maximum length. Each individual
%         length computation yields a pair of calipers to the object
%         orientated at every 5 degrees. The object is propagated into
%         an image containing the union of these calipers to obtain an
%         image of the bounding polygon. 
%         
% NUMBER OF CLASSES
% 
%         4       OPEL, SAAB, BUS, VAN
% 
% NUMBER OF EXAMPLES
% 
%                 Total no. = 946
%                 
%                 No. in each class
%                 
%                   opel 240
%                   saab 240
%                   bus  240
%                   van  226
%                 
%                 
%                 100 examples are being kept by Strathclyde for validation.
%                 So StatLog partners will receive 846 examples.
% 
% NUMBER OF ATTRIBUTES
% 
%                 No. of atts. = 18
% 
%         
% BIBLIOGRAPHY
% 
%           Turing Institute Research Memorandum TIRM-87-018 "Vehicle
%          Recognition Using Rule Based Methods" by Siebert,JP (March 1987)
% 
% 
@relation vehicle

@attribute 'COMPACTNESS' real
@attribute 'CIRCULARITY' real
@attribute 'DISTANCE CIRCULARITY' real
@attribute 'RADIUS RATIO' real
@attribute 'PR.AXIS ASPECT RATIO' real
@attribute 'MAX.LENGTH ASPECT RATIO' real
@attribute 'SCATTER RATIO' real
@attribute 'ELONGATEDNESS' real
@attribute 'PR.AXIS RECTANGULARITY' real
@attribute 'MAX.LENGTH RECTANGULARITY' real
@attribute 'SCALED VARIANCE_MAJOR' real
@attribute 'SCALED VARIANCE_MINOR' real
@attribute 'SCALED RADIUS OF GYRATION'  real
@attribute 'SKEWNESS ABOUT_MAJOR' real
@attribute 'SKEWNESS ABOUT_MINOR' real
@attribute 'KURTOSIS ABOUT_MAJOR' real
@attribute 'KURTOSIS ABOUT_MINOR' real
@attribute 'HOLLOWS RATIO' real
@attribute 'Class' {opel,saab,bus,van}

@data
95,48,83,178,72,10,162,42,20,159,176,379,184,70,6,16,187,197,van
91,41,84,141,57,9,149,45,19,143,170,330,158,72,9,14,189,199,van
104,50,106,209,66,10,207,32,23,158,223,635,220,73,14,9,188,196,saab