|1. TITLE: | Vehicle silhouette dataset | | 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. | | |2. USE IN STATLOG | | 2.1- Testing Mode | 9-Fold Cross Validation | | 2.2- Special Preprocessing | No | | 2.3- Test Results | Success Rate TIME | Algorithm Train Test Train Test | -------------------------------------------- | QuaDisc 91.5 85.000 251 29 | Dipol92 ? 84.900 ? 1 | Alloc80 100 82.700 30 10 | LogDisc 83.3 80.800 758 8 | BackProp 83.2 79.300 14411 4 | Discrim 79.8 78.400 16 3 | Smart 93.8 78.300 3017 1 | Cart ? 76.500 29 1 | C4.5 93.5 73.400 153 1 | BayTree ? 72.900 4 1 | KNN 100 72.500 164 23 | Cal5 70.3 72.100 41 1 | Cascade ? 72.000 ? 1 | LVQ ? 71.300 ? ? | Ac2 ? 70.400 595 23 | IndCart 95.3 70.200 85 1 | NewId 97 70.200 18 1 | Radial 90.2 69.300 1736 12 | Cn2 98.2 68.600 100 1 | Itrule ? 67.600 985 ? | Kohonen 88.5 66.000 5962 50 | Castle 49.5 49.500 23 3 | Bayes 48.1 44.200 4 1 | Default ? 25.000 | | |3. SOURCES and PAST USAGE | | ORIGINAL SOURCE | Drs.Pete Mowforth and Barry Shepherd | Turing Institute | George House | 36 North Hanover St. | Glasgow | G1 2AD | | This dataset comes from the Turing Institute, Glasgow, Scotland. | If you use this dataset in any publication please acknowledge this | source. | | 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. | |4. ATTRIBUTE DISCRIPTION | | NUMBER OF EXAMPLES | | Total no. = 846 | (946 in original dataset, 100 examples | were kept by Strathclyde for validation. | So the dataset issued contains only 846 examples). | NUMBER OF CLASSES | | 4 OPEL, SAAB, BUS, VAN | | Class Nr.Examples | -------------------- | 1 212 (25.06%) | 2 217 (25.65%) | 3 218 (25.77%) | 4 199 (23.52%) | | NUMBER OF ATTRIBUTES | | No. of atts. = 18 | | 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. | | |BIBLIOGRAPHY | | Turing Institute Research Memorandum TIRM-87-018 "Vehicle | Recognition Using Rule Based Methods" by Siebert,JP (March 1987) | | | |CONTACTS | statlog-adm@ncc.up.pt | bob@stams.strathclyde.ac.uk | | |================================================================================ | 1,2,3,4. COMPACTNESS: continuous. CIRCULARITY: continuous. DISTANCE CIRCULARITY: continuous. RADIUS RATIO: continuous. PR AXIS ASPECT RATIO: continuous. MAX LENGTH ASPECT RATIO: continuous. SCATTER RATIO: continuous. ELONGATEDNESS: continuous. PR AXISRECTANGULAR: continuous. LENGTHRECTANGULAR: continuous. MAJORVARIANCE: continuous. MINORVARIANCE: continuous. GYRATIONRADIUS: continuous. MAJORSKEWNESS: continuous. MINORSKEWNESS: continuous. MINORKURTOSIS: continuous. MAJORKURTOSIS: continuous. HOLLOWS RATIO: continuous.