Entropy-based 2D image dissimilarity measure

Authors

Meng-Hung Wu, University of Texas-Pan American

Date of Award

8-2005

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Ping-Sing Tsai

Second Advisor

Dr. Zhixiang Chen

Third Advisor

Dr. Peter A. Ng

Abstract

Traditional histogram or statistics based 2D image similarity/dissimilarity metrics fail to handle conjugate pair of black and white images, due to the lack of spatial information in the measurement. Recently proposed Compression-based Dissimilarity Measure (CDM) [1] based on the concept of Kolmogorov complexity has provided a different paradise for similarity measurement. However, without a clear definition how to “concatenate” two 2D images, CDM has difficulties to directly apply with 2D images. In this thesis, an entropy -based 2D image dissimilarity measure is proposed within the same Kolmogorov complexity paradise. The spatial relationship between images is embedded in our metric, and the actual compression of images is not needed once the entropy values are obtained. The proposed metric has been tested for scene change detection application, and encouraging results are presented here.

Comments

Copyright 2005 Meng-Hung Wu. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/entropy-based-2d-image-dissimilarity-measure/docview/305371008/se-2?accountid=7119

Granting Institution

University of Texas-Pan American