BIB-VERSION:: CS-TR-v2.0
ID:: ncstrl.dartmouthcs//TR2004-520
ENTRY:: October 29, 2004
ORGANIZATION:: Dartmouth College, Computer Science
TITLE:: Mercer Kernels for Object Recognition with Local Features
TYPE:: Technical Report (paper)
REVISION:: 1
AUTHOR:: Lyu, Siwei
DATE:: October 2004
RETRIEVAL:: For a paper copy, email <reports@cs.dartmouth.edu>
RETRIEVAL:: For a paper copy, write to
       Technical Report Librarian
       Department of Computer Science
       Dartmouth College
       6211 Sudikoff Laboratory
       Hanover, NH 03755-3510
       USA
RETRIEVAL:: PDF at http://www.cs.dartmouth.edu/reports/TR2004-520.pdf
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.
NOTE:: 
Submitted to CVPR 2005.
END:: ncstrl.dartmouthcs//TR2004-520