Abstract

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 difierent learning algorithms for visual recognition. Large margin classi- fiers, such as SNoW and SVM, have recently demonstrated their success in ob ject detection and recognition. In this paper, we present a theo- retical account of these two learning approaches, and their suitability to visual recognition. Using tools from computational learning theory, we show that the main difierence 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 experi- ments. Experimental results exhibit good generalization and robustness properties of the SNoW-based method, and conform to the theoretical analysis.