Universiti Teknologi Malaysia Institutional Repository

Images information retrieval using Gustafson-Kessel relevance feedback

Zainuddin, Nurulhuda (2006) Images information retrieval using Gustafson-Kessel relevance feedback. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System.

[img] PDF (Abstract)
[img] PDF (1st Chapter)
[img] PDF (References)
[img] PDF (Table of Contents)


The goal of CBIR is to retrieve images that are visually similar to the query image. Relevance feedback retrieval systems ask the user for feedback on retrieval results and then use this feedback on later retrievals with the goal of increasing retrieval performance. The objectives of this research are to compare CBIR based with Gustafson-Kessel (GK) clustering and relevant feedback approach and to evaluate the effectiveness of GK relevance feedback for images retrieval. The research requires a better understanding of GK clustering and probabilistic relevance feedback method in turn to figure out different methods that can be used in solving similar problems. This project will give better insights in the usage of relevance feedback learning in order to reduce the gap between low-level features and high-level human concepts. The research will evaluate Gustafson-Kessel clustering and probabilistic relevance feedback method to improve the retrieval performance.

Item Type:Thesis (Masters)
Additional Information:Thesis (Master of Science (Computer Science)) - Universiti Teknologi Malaysia, 2006; Supervisor : Dr. Ali Bin Selamat
Uncontrolled Keywords:Gustafson-Kessel (GK) clustering , image retrieval systems, query image, relevance feedback, Content-Based Image Retrieval
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:5383
Deposited By: Ms Zalinda Shuratman
Deposited On:09 Apr 2008 03:52
Last Modified:24 Oct 2013 01:35

Repository Staff Only: item control page