Abstract: Over the past few years, various clustering approaches have been applied to the problem of image database categorization and have proven to be effective. The resulting clusters have been used to index the image database and reduce the search space during the retrieval process. They have also been used to help the user navigate through the database. However, despite recent progress, image database categorization remains a difficult research task. The problem is more acute when the high dimensional feature space, encoding the low level image features, is corrupted by noise. In this project we will design and  implement an image database  categorization system based on unsupervised learning techniques of image’s low-level features.
 
The project will include programming using MATLAB to extract low-level features,  implement clustering algorithms to group images into homogeneous clusters, and eventually integrate an image retrieval layer to the system 

This is an introductory level graduate course in the design and analysis of algorithms. The aim of the course is to provide a solid background in designing and analyzing  algorithms. It is hoped that a student will be able to analyze and compare algorithms based on their efficiency, and also design   efficient algorithms using several algorithm design paradigms.

This course continues the coverage of the fundamental concepts of Object Oriented Programming that started in Java Programming I (CSC 111). 

In the Java Programming I, considerable time is spent concentrating on the fundamental programming concepts such as declarations of variables and basic control structures, methods and arrays, prior to introducing students to the concepts of classes and objects.

The Java Programming II covers more advanced topics such as packages, inheritance, polymorphism, interfaces, error handling view exceptions, file-handling techniques, generics, graphics and event-driven programming and data structures in which discusses the techniques used to create and manipulate dynamic data structures, such as linked lists, stacks and queues.