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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
79110 Freiburg, Germany
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