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arff slurped Weka

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# Instances: 846 / # Attributes: 19
HDF5 (118.9 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

vehicle

Version mldata

0

Comment

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!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: 041 552 4400 x3033

    Fax: 041 552 4711 

    e-mail: alistair@uk.ac.strathclyde.stams

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)

Names

COMPACTNESS,CIRCULARITY,DISTANCE CIRCULARITY,RADIUS RATIO,PR.AXIS ASPECT RATIO,MAX.LENGTH ASPECT RATIO,SCATTER RATIO,ELONGATEDNESS,PR.AXIS RECTANGULARITY,MAX.LENGTH RECTANGULARITY,

Types

1. numeric
2. numeric
3. numeric
4. numeric
5. numeric
6. numeric
7. numeric
8. numeric
9. numeric
10. numeric

Data (first 10 data points)

COMP...	CIRC...	DIST...	RADI...	PR.A...	MAX....	SCAT...	ELON...	PR.A...	MAX....	...
95	48	83	178	72	10	162	42	20	159	...
91	41	84	141	57	9	149	45	19	143	...
104	50	106	209	66	10	207	32	23	158	...
93	41	82	159	63	9	144	46	19	143	...
85	44	70	205	103	52	149	45	19	144	...
107	57	106	172	50	6	255	26	28	169	...
97	43	73	173	65	6	153	42	19	143	...
90	43	66	157	65	9	137	48	18	146	...
86	34	62	140	61	7	122	54	17	127	...
93	44	98	197	62	11	183	36	22	146	...
...	...	...	...	...	...	...	...	...	...	...

Description

A jarfile containing 37 classification problems, originally obtained from the UCI repository (datasets-UCI.jar, 1,190,961 Bytes).

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http://www.ics.uci.edu/~mlearn/MLRepository.html

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by mldata on 2010-11-06 09:57

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