Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An Experimental Comparison of Appearance and Geometric Model Based Recognition
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
Object Recognition Using Appearance-Based Parts and Relations
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
International Journal of Knowledge-based and Intelligent Engineering Systems
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This paper investigates the performance of Support Vector Machines with linear, quadratic and cubic kernels in the problem of recognising 3D objects from 2D views. It describes an experiment using the complete set of images from the Columbia Coil100 image database. Image views were randomly selected from the object classes. Previous works used only subsets of the classes, from which only a few training and testing set sizes were extracted and object views were usually too close to each other, which may have artificially increased the recognition rates. In our experiments, we observed that the degree of the polynomial kernel played a minor role in the final results. Moreover, although recognition rates were slightly inferior to those of previous work, a clearer picture of the SVM performance on the Coil100 image database has been produced.