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ECCV
2002
Springer

A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition

10 years 1 days ago
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition
Numerous statistical learning methods have been developed for visual recognition tasks. Few attempts, however, have been made to address theoretical issues, and in particular, study the suitability of different learning algorithms for visual recognition. Large margin classifiers, such as SNoW and SVM, have recently demonstrated their success in object detection and recognition. In this paper, we present a theoretical account of these two learning approaches, and their suitability to visual recognition. Using tools from computational learning theory, we show that the main difference between the generalization bounds of SVM and SNoW depends on the properties of the data. We argue that learning problems in the visual domain have sparseness characteristics and exhibit them by analyzing data taken from face detection experiments. Experimental results exhibit good generalization and robustness properties of the SNoW-based method, and conform to the theoretical analysis.
Ming-Hsuan Yang, Dan Roth, Narendra Ahuja
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2002
Where ECCV
Authors Ming-Hsuan Yang, Dan Roth, Narendra Ahuja
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