Most of my research falls under the umbrella
of Machine Learning and Computer Vision.
The focus of my
recent research has been on computer-based image segmentation,
which is a key step in computer vision.
Computer vision is concerned with the theory
for building artificial systems that obtain
information from images, i.e., machines that
can "see". It is a relatively young field
that attempts to emulate the extremely
complex human visual system by using image
capturing equipment as the "eyes" and
computers and algorithms as the "brain". A
fundamental task of the human visual system
is the ability to find objects in an
image, i.e., segmenting an image into
semantically meaningful regions. Because of the semantic gap, which refers to the disconnection between low-level visual features and high-level semantics, the development of strong image segmentation algorithms capable of generating regions that correspond to semantically-meaningful objects has been an area of considerable research activity.
My
Ph.D. dissertation investigated the
possibility of exploiting long-term learning
to improve the performance of content-based
image retrieval (CBIR). CBIR has been an area of
intensive research. It aims at retrieval of
images from a database that are relevant to
a query image based on automatically derived
low-level visual features. The
relevance of a database image to the query
image is proportional to the distance
between their corresponding points in the
feature space. Unfortunately, human notion
of similarity is usually based on high-level
abstractions such as activities, events, or
emotions displayed in an image. As a result,
images with high feature similarity to the
query image may be completely different from
it in terms of semantics. Thus, the semantic
gap has also been an open challenging problem in
CBIR.
The
focus of my master thesis was on the
development of a parallel genetic
algorithm
for the graph
theory/combinatorial problem of finding
Ramsey
Numbers.
Please look at my list of
publications to see what I am currently
thinking about.
