MPhil Thesis Defence


"Integrating Distance Function Learning and Support Vector
Machine for Content-Based Image Retrieval"

By

Mr. Siu-Shing Tang


Abstract

Content-Based Image Retrieval (CBIR) is an important research area that
can bring about significant implications to the retrieval of images. The
basic idea of CBIR is to search for and retrieve images by presenting one
or more query images as examples, instead of performing text-based search
based on the metadata about the images. An important implication is that
CBIR does not require the tedious, manual process of providing metadata
about the images in the image database. Also, it allows the user to pose
queries without having to know the textual descriptions of the images.
Moreover, many useful features in images, such as the texture of wood, can
hardly be described satisfactorily by metadata in the form of textual
descriptions. CBIR, on the other hand, typically represents the contents
of images in the form of feature vectors. Unfortunately, sometimes such
feature vectors cannot correctly and uniquely depict all the possible
image interpretations from a human's perspective. This reveals the fact
that low-level feature vector representations and high-level semantics of
the images are not linked strongly, which is often referred to as the
semantic gap problem.

To address the semantic gap problem, recent attempts have been made and
they can roughly be categorized into two main approaches: metric learning
and support vector machine (SVM). Metric learning methods aim at learning
a better metric so that the corresponding dissimilarity measure can become
more accurate in reflecting the underlying semantics. On the other hand,
the SVM classification approach is primarily based on a relevance feedback
(RF) process within each query session. We observe that these two
approaches are complementary, and hence we propose in this thesis a CBIR
framework which integrates the two approaches to get the best of both
worlds. In many real-world applications, such as photo-taking, we notice
that images that are close together in the image space are likely to be
images taken at the same location with similar objects involved. Based on
this observation, we relax the restrictions of metrics and develop an
algorithm called Distance Function Learning with Neighborhood
Generalization (DFLNG). By integrating DFLNG with SVM based active
learning, we observe that the image retrieval effectiveness can be
significantly increased.


Date:				Tuesday, 28 November 2006

Time:				10:00a.m.-12:00noon

Venue:				Room 3401
				Lifts 17-18

Committee Members:		Dr. Dit-Yan Yeung (Supervisor)
				Dr. Qiang Yang (Chairperson)
				Dr. David Rossiter


**** ALL are Welcome ****