Data
vehicle

vehicle

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • study_14 study_1 study_1695 study_3547 study_4992 study_8366 study_12699 study_1524 study_6323 study_8366 study_12756 study_2018 study_2981 study_8366 study_6303 study_6454 study_8366 study_13152 study_3297 study_4051 study_8366 study_1981 study_8366 study_3399 study_4671 study_6019 study_6416 study_6824 study_10167 study_10410 study_11660 study_3051 study_6535 study_11460 study_13281 study_3367 study_10848 study_13369 study_308 study_688 study_6748 study_842 study_11009 study_12664 study_1545 study_1739 study_2752 study_3443 study_3583 study_6516 study_11389 study_13133
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Peter Mowforth Source: UCI - Please cite: Siebert,JP. Turing Institute Research Memorandum TIRM-87-018 "Vehicle Recognition Using Rule Based Methods" (March 1987) 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_min3 MAJOR AXIS SKEWNESS ABOUT (3rd order moment about minor axis)/sigma_maj3 MINOR AXIS KURTOSIS ABOUT (4th order moment about major axis)/sigma_min4 MINOR AXIS KURTOSIS ABOUT (4th order moment about minor axis)/sigma_maj4 MAJOR AXIS HOLLOWS RATIO (area of hollows)/(area of bounding polygon) Where sigma_maj2 is the variance along the major axis and sigma_min2 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

19 features

Class (target)nominal4 unique values
0 missing
COMPACTNESSnumeric44 unique values
0 missing
CIRCULARITYnumeric27 unique values
0 missing
DISTANCE_CIRCULARITYnumeric63 unique values
0 missing
RADIUS_RATIOnumeric134 unique values
0 missing
PR.AXIS_ASPECT_RATIOnumeric37 unique values
0 missing
MAX.LENGTH_ASPECT_RATIOnumeric21 unique values
0 missing
SCATTER_RATIOnumeric131 unique values
0 missing
ELONGATEDNESSnumeric35 unique values
0 missing
PR.AXIS_RECTANGULARITYnumeric13 unique values
0 missing
MAX.LENGTH_RECTANGULARITYnumeric66 unique values
0 missing
SCALED_VARIANCE_MAJORnumeric128 unique values
0 missing
SCALED_VARIANCE_MINORnumeric424 unique values
0 missing
SCALED_RADIUS_OF_GYRATIONnumeric143 unique values
0 missing
SKEWNESS_ABOUT_MAJORnumeric39 unique values
0 missing
SKEWNESS_ABOUT_MINORnumeric23 unique values
0 missing
KURTOSIS_ABOUT_MAJORnumeric41 unique values
0 missing
KURTOSIS_ABOUT_MINORnumeric30 unique values
0 missing
HOLLOWS_RATIOnumeric31 unique values
0 missing

107 properties

846
Number of instances (rows) of the dataset.
19
Number of attributes (columns) of the dataset.
4
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
18
Number of numeric attributes.
1
Number of nominal attributes.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4
Average number of distinct values among the attributes of the nominal type.
0.25
First quartile of skewness among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.32
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.04
Mean skewness among attributes of the numeric type.
6.17
First quartile of standard deviation of attributes of the numeric type.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.32
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
25.77
Percentage of instances belonging to the most frequent class.
22.77
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2
Entropy of the target attribute values.
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
218
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.44
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.98
Minimum kurtosis among attributes of the numeric type.
87.88
Second quartile (Median) of means among attributes of the numeric type.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
58.38
Maximum kurtosis among attributes of the numeric type.
6.38
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
439.91
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.5
Second quartile (Median) of skewness among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
8.06
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
-0.23
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
0.16
Third quartile of kurtosis among attributes of the numeric type.
0.26
Average class difference between consecutive instances.
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.78
Maximum skewness among attributes of the numeric type.
2.59
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
178.18
Third quartile of means among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
176.69
Maximum standard deviation of attributes of the numeric type.
23.52
Percentage of instances belonging to the least frequent class.
94.74
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
199
Number of instances belonging to the least frequent class.
5.26
Percentage of nominal attributes.
0.79
Third quartile of skewness among attributes of the numeric type.
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
5.15
Mean kurtosis among attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
31.68
Third quartile of standard deviation of attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
117.63
Mean of means among attributes of the numeric type.
0.54
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.78
First quartile of kurtosis among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.32
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
35.85
First quartile of means among attributes of the numeric type.

11 tasks

8 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
Define a new task