Gradient theoretic clustering: a theoretic model

Thomas G. Allen

doi:10.1117/12.540756

15 July 2004 Gradient theoretic clustering: a theoretic model

Thomas G. Allen

Proceedings Volume 5438, Visual Information Processing XIII; (2004) https://doi.org/10.1117/12.540756
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

The research discussed in this paper is a continuation of the author's previous research published in SPIE's Visual Information Processing Proceedings of 2003 [1] entitled "Improving the Performance of Content-Based Image Retrieval Systems". The SPIE article discussed a new method for clustering an image database based on level one similarity using a new technique called the "enumeration of gradient states". This technique is based on the direction of the gradient (converting the gradient into pixel moments and computing a value known as the "gradient spin excess" for determining the complexity level of an image). This complexity level was used for clustering images into similarity groupings. From this similarity grouping or clustering, level one similarity retrieval was improved by searching each cluster for the proper membership in a cluster rather than searching the whole database. This article expands the previous study with a theoretical discussion showing that complexity based clustering using gradient spin excess is directly related to the degree of randomness (entropy) of pixel moments. In addition, we propose an improved gradient states methodology by calculating the pixel moments of an image at various sub-block sizes and clustering the image database based on hierarchical clustering using level one similarity. Finally it is shown theoretically as well as experimentally that the speed of similarity retrieval is of complexity O(n), a definite improvement over the traditional color histogram (L1-norm) similarity retrieval method.

Citation Download Citation

Thomas G. Allen "Gradient theoretic clustering: a theoretic model", Proc. SPIE 5438, Visual Information Processing XIII, (15 July 2004); https://doi.org/10.1117/12.540756

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Databases

Image retrieval

Feature extraction

Fuzzy logic

Image enhancement

Information theory

Visual information processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years