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.
|Title of host publication||Computer Vision - ECCV 2002 - 7th European Conference on Computer Vision, Proceedings|
|Editors||Anders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen|
|Number of pages||15|
|Publication status||Published - 2002|
|Event||7th European Conference on Computer Vision, ECCV 2002 - Copenhagen, Denmark|
Duration: 2002 May 28 → 2002 May 31
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||7th European Conference on Computer Vision, ECCV 2002|
|Period||02/5/28 → 02/5/31|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)