\documentclass{icai}
\begin{document}

\title{Enhancing content-based image retrieval}

\author{Lajos Koll\'ar\AND Kriszti\'an Ver\'eb}
\institute{Department of Information Technology\\Faculty of Informatics\\University of Debrecen\\ e-mail: \{kollarl,sparrow\}@inf.unideb.hu}
\address{\textbf{Lajos Koll\'ar, Kriszti\'an Ver\'eb}\\ H--4032 Debrecen, Egyetem t\'er 1.}

\begin{ICAIpaper}
\begin{abstract}
Given an image and some aspects, content-based image retrieval is used for finding similar images to the image. This is usually done by extracting the values of some predefined features which are attributes of all images (such that colour, shape and texture), and then apply a similarity measure (and possibly a confidence bound). These features have a static structure so they can be easily represented in the relational model. Image databases built on relational or object-relational DBMSs are based on this idea.

In real world, there are more features of an image which cannot be predefined either. For example, some images contain text, others contain people (or both), etc. These are pieces of information which can easily be extracted by a human being if we ask her/him to describe the picture. That is not the case when we replace humans with computers. To achieve something similar, we need some image processing algorithms to do the work but due to the lack of intelligence its usage is limited a bit. We need an OCR algorithm for recognizing the text and a face detection algorithm for counting the number of people in the picture. Similarly to the predefined features, these are metadata of the image which can be extracted and stored. Since these data are semi-structured, it is very hard to represent in the relational model therefore we use XML for data storage.

In our lecture we will introduce the concepts of such an XML-based complex image processing and querying system based on Oracle 10\textsl{g}, where the efficiency of query methods can be enhanced using the above mentioned additive information.
\keywords Image database, feature vectors, XML, Oracle
%\AMSclassificationnumber
\end{abstract}

\end{ICAIpaper}
\end{document}
