Bag classification using support vector machines

Uri Kartoun, Helman Stern, Yael Edan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Scopus citations


This paper describes the design of multi-category support vector machines (SVMs) for classification of bags. To train and test the SVMs a collection of 120 images of different types of bags were used (backpacks, small shoulder bags, plastic flexible bags, and small briefcases). Tests were conducted to establish the best polynomial and Gaussian RBF (radial basis function) kernels. As it is well known that SVMs are sensitive to the number of features in pattern classification applications, the performance of the SVMs as a function of the number and type of features was also studied. Our goal here, in feature selection is to obtain a smaller set of features that accurately represent the original set. A Kfold cross validation procedure with three subsets was applied to assure reliability. In a kernel optimization experiment using nine popular shape features (area, bounding box ratio, major axis length, minor axis length, eccentricity, equivalent diameter, extent, roundness and convex perimeter), a classification rate of 95% was achieved using a polynomial kernel with degree six, and a classification rate of 90% was achieved using a RBF kernel with 27 sigma. To improve these results a feature selection procedure was performed. Using the optimal feature set, comprised of bounding box ratio, major axis length, extent and roundness, resulted in a classification rate of 96.25% using a polynomial kernel with degree of nine. The collinearity between the features was confirmed using principle component analysis, where a reduction to four components accounted for 99.3% of the variation for each of the bag types.

Original languageEnglish
Title of host publicationApplied Soft Computing Technologies
Subtitle of host publicationThe Challenge of Complexity
EditorsAjith Abraham, Bernard Baets, Mario Koeppen, Bertram Nickolay
Number of pages10
StatePublished - 18 Dec 2006

Publication series

NameAdvances in Soft Computing
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mechanics
  • Computer Science Applications


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