@InProceedings{10.20532/ccvw.2013.0011,
  author =       {Valentina Zadrija and Sini{\v s}a {\v S}egvi{\' c}},
  title =        {Multiclass Road Sign Detection using Multiplicative
                  Kernel},
  booktitle =    {Proceedings of the Croatian Compter Vision Workshop,
                  Year 1},
  pages =        {37-42},
  year =         2013,
  editor =       {Lon{\v c}ari{\' c}, Sven and {\v S}egvi{\' c},
                  Sini{\v s}a},
  address =      {Zagreb},
  month =        {September},
  organization = {Center of Excellence for Computer Vision},
  publisher =    {University of Zagreb},
  abstract =     {We consider a problem of multiclass road sign
                  detection using multiplicative kernel comprised from
                  two kernels. We show that problems of detection and
                  within-foreground classification can be jointly
                  solved by using one kernel to measure object -
                  background differences and another one to account
                  for within-class variations. The main idea behind
                  this approach is that road signs from different
                  foreground variations can share features that
                  discriminate them from backgrounds. As a model, we
                  use SVM classifier, thus feature sharing is obtained
                  through support vector sharing. Training yields a
                  family of linear detectors, where each detector
                  corresponds to a specific foreground training
                  sample. However, there may be redundancy between
                  various detectors, which is accounted for using
                  k-medoids clustering technique. Finally, we report
                  detection and classification results on a set of
                  road sign images obtained from a camera on a moving
                  vehicle.},
  doi =          {10.20532/ccvw.2013.0011},
  url =          {https://doi.org/10.20532/ccvw.2013.0011}
}