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Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel
 

Summary: Kernel Biased Discriminant Analysis
Using Histogram Intersection Kernel
for Content-Based Image Retrieval
Lin Mei, Gerd Brunner, Lokesh Setia, and Hans Burkhardt
Chair of Pattern Recognition and Image Processing
Computer Science Department
Albert-Ludwigs-University
79110 Freiburg, Germany
{mei,gbrunner,setia,burkhardt}@informatik.uni-freiburg.de
Abstract. It is known that no single descriptor is powerful enough to encompass
all aspects of image content, i.e. each feature extraction method has its own view
of the image content. A possible approach to cope with that fact is to get a whole
view of the image(object). Then using machine learning approach from user's
Relevance feedback to obtain a reduced feature. In this paper, we concentrate
on some points about Biased Discriminant Analysis / Kernel Biased Discrimi-
nant Analysis (BDA/KBDA) based machine learning approach for CBIR. The
contributions of this paper are: 1. using generalized singular value decomposition
(GSVD) based approach solve the small sample size problem in BDA/KBDA and
2. using histogram intersection as a kernel for KBDA. Experiments show that this
kind of kernel gets improvement compare to other common kernels.

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences