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Description

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. 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 classification 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 Neighbour) 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.

Related Papers

• H. Altay Guvenir. A Classification Learning Algorithm Robust to Irrelevant Features. Bilkent University, Department of Computer Engineering and Information Science. [link]
• Jeffrey P. Bradford and Clayton Kunz and Ron Kohavi and Clifford Brunk and Carla Brodley. Appears in ECML-98 as a research note Pruning Decision Trees with Misclassification Costs. School of Electrical Engineering. [link]
• Gisele L. Pappa and Alex Alves Freitas and Celso A A Kaestner. AMultiobjective Genetic Algorithm for Attribute Selection. Computing Laboratory Pontificia Universidade Catolica do Parana University of Kent at Canterbury. [link]
• Maria Salamo and Elisabet Golobardes. Analysing Rough Sets weighting methods for Case-Based Reasoning Systems. Enginyeria i Arquitectura La Salle. [link]
• Ron Kohavi and George H. John. Automatic Parameter Selection by Minimizing Estimated Error. Computer Science Dept. Stanford University. [link]
• Thomas G. Dietterich. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning, 40. 2000. [link]
• Ronaldo C. Prati and Peter A. Flach. ROCCER: an Algorithm for Rule Learning Based on ROC Analysis. Institute of Mathematics and Computer Science University of S~ ao Paulo. [link]
• Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [link]
• Dmitry Pavlov and Alexandrin Popescul and David M. Pennock and Lyle H. Ungar. Mixtures of Conditional Maximum Entropy Models. ICML. 2003. [link]
• Ping Zhong and Masao Fukushima. A Regularized Nonsmooth Newton Method for Multi-class Support Vector Machines. 2005. [link]
• Vikas Sindhwani and P. Bhattacharya and Subrata Rakshit. Information Theoretic Feature Crediting in Multiclass Support Vector Machines. [link]
• Ron Kohavi. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. IJCAI. 1995. [link]
• Adil M. Bagirov and Alex Rubinov and A. N. Soukhojak and John Yearwood. Unsupervised and supervised data classification via nonsmooth and global optimization. School of Information Technology and Mathematical Sciences, The University of Ballarat. [link]
• Yin Zhang and W. Nick Street. Bagging with Adaptive Costs. Management Sciences Department University of Iowa Iowa City. [link]
• Rajesh Parekh and Jihoon Yang and Vasant Honavar. Constructive Neural-Network Learning Algorithms for Pattern Classification. [link]
• Ron Kohavi and Mehran Sahami. Error-Based and Entropy-Based Discretization of Continuous Features. KDD. 1996. [link]
• Chih-Wei Hsu and Cheng-Ru Lin. A Comparison of Methods for Multi-class Support Vector Machines. Department of Computer Science and Information Engineering National Taiwan University. [link]
• Remco R. Bouckaert and Eibe Frank. Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. PAKDD. 2004. [link]
• Richard Maclin. Boosting Classifiers Regionally. AAAI/IAAI. 1998. [link]
• James Bailey and Thomas Manoukian and Kotagiri Ramamohanarao. Fast Algorithms for Mining Emerging Patterns. PKDD. 2002. [link]
• Gisele L. Pappa and Alex Alves Freitas and Celso A A Kaestner. Attribute Selection with a Multi-objective Genetic Algorithm. SBIA. 2002. [link]
• Robert E. Schapire and Yoav Freund and Peter Bartlett and Wee Sun Lee. The Annals of Statistics, to appear. Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. AT&T; Labs. 1998. [link]
• Thierry Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 30. 2000. [link]
• Robi Polikar and L. Upda and S. S. Upda and Vasant Honavar. Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 31. 2001. [link]
• Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005. [link]
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