The  Weizmann  Institute  of  Science
                  Faculty of Mathematics and Computer Science

                          Vision and Robotics Seminar

                                  Shai Avidan
                            Interdisciplinary Center

                                 will speak on

                              Subset Selection for
                  Computationally Efficient SVM Classification

We update the {\bf SVM} score of an object through a video sequence with a small and variable
subset of support vectors. In the first frame we use all the support vectors to compute the {\bf SVM}
score of the object but in subsequent frames we use only a small and variable subset of support
vectors to update the {\bf SVM} score. In each frame we calculate the dot-products of the support
vectors in the subset with the pattern of the object in the current frame. The difference in the
dot-products, between past and current frames, is used to update the {\bf SVM} score. This is done
at a fraction of the computational cost required to re-evaluate the {\bf SVM} score from scratch in
every frame. The two methods we develop are ``loop unrolling", in which we break the set of all
support vectors into subsets of equal size and use them cyclically, and ``Maximum Variance Subset
Selection'', in which we choose the support vectors whose dot-product with the test pattern varied
the most in previous frames. We combine this techniques together for the problem of maintaining the
{\bf SVM} score of objects through a video sequence. Results on real video sequences are shown.

                      The lecture will take place in the 
                     Lecture Hall, Room 1, Ziskind Building
                          on Thursday, January 9, 2003
                                    at 11:00