The Repository @ St. Cloud State

Open Access Knowledge and Scholarship

Date of Award

12-2016

Culminating Project Type

Thesis

Degree Name

Computer Science: M.S.

Department

Computer Science and Information Technology

College

School of Science and Engineering

First Advisor

Jayantha Herath

Second Advisor

Ezzat Kirmani

Third Advisor

Susantha Herath

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Content Based Image Retrieval, CBIR, Image Comparison, Color Histogram, Image Retrieval, Image Processing

Abstract

Content Based Image Retrieval (CBIR) is still a major research area due to its complexity and the growth of the image databases. Color Based Image Retrieval is one of the major retrieval methods in Content Based Image Retrieval systems. At present, researchers combine image retrieval techniques to get more accurate results. With the large image databases, image retrieval is still a challenging area and the efficiency of the image retrieval techniques still need to be considered. For this purpose, a comparative study of image retrieval techniques has been discussed in this paper. In addition, an efficient method is presented which aids to retrieve images by storing an intermediate result of the process in the database. To compare the query image and the images in the database, Euclidean distance, Normalized Cross Correlation distance and Histogram Intersection distance are taken as distance measures. Experimental results demonstrate Histogram Intersection distance is better than the other two methods. The intermediate result was stored using an event in the system. By making minor modifications to the proposed system, it creates a possibility for the user to add images to the database just by clicking on a button. Thus, the user can expand his/her database on his/her own will. Results show a significant improvement of performance in the proposed method.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.