@TechReport{Dartmouth:TR2004-520,
  author = {Siwei Lyu},
  title = {{Mercer Kernels for Object Recognition with Local Features}},
  institution = {Dartmouth College, Computer Science},
  address = {Hanover, NH},
  number = {TR2004-520},
  year = {2004},
  month = {October},
  URL = {http://www.cs.dartmouth.edu/reports/TR2004-520.pdf},
  comment = {
    Submitted to CVPR 2005.
  },
  abstract = {
    In this paper, we propose a new class of kernels for object
    recognition based on local image feature representations.  Formal
    proofs are given to show that these kernels satisfy the Mercer
    condition and reflect similarities between sets of local features.  In
    addition, multiple types of local features and semilocal constraints
    are incorporated to reduce mismatches between local features, thus
    further improve the classification performance.  Experimental results
    of SVM classifiers coupled with the proposed kernels are reported on
    ecognition tasks with the standard COIL-100 database and compared
    with existing methods.  The proposed kernels achieved satisfactory
    performance and were robust to changes in object configurations and
    image degradations.
  }
}