TY - GEN
T1 - A tale of two classifiers
T2 - 7th European Conference on Computer Vision, ECCV 2002
AU - Yang, Ming Hsuan
AU - Roth, Dan
AU - Ahuja, Narendra
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84937539670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937539670&partnerID=8YFLogxK
U2 - 10.1007/3-540-47979-1_46
DO - 10.1007/3-540-47979-1_46
M3 - Conference contribution
AN - SCOPUS:84937539670
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 685
EP - 699
BT - Computer Vision - ECCV 2002 - 7th European Conference on Computer Vision, Proceedings
A2 - Heyden, Anders
A2 - Sparr, Gunnar
A2 - Nielsen, Mads
A2 - Johansen, Peter
PB - Springer
Y2 - 28 May 2002 through 31 May 2002
ER -