Image clustering using fuzzy graph theory

Published

Conference Paper

We propose an image clustering algorithm which uses fuzzy graph theory. First, we define a fuzzy graph and the concept of connectivity for a fuzzy graph. Then, based on our definition of connectivity we propose an algorithm which finds connected subgraphs of the original fuzzy graph. Each connected subgraph can be considered as a cluster. As an application of our algorithm, we consider a database of images. We calculate a similarity measure between any pairs of images in the database and generate the corresponding fuzzy graph. Then, we find the subgraphs of the resulting fuzzy graph using our algorithm. Each subgraph corresponds to a cluster. We apply our image clustering algorithm to the key frames of news programs to find the anchorperson clusters. Simulation results show that our algorithm is successful to find most of anchorperson frames from the database.

Duke Authors

Cited Authors

  • Jafarkhani, H; Tarokh, V

Published Date

  • January 1, 2000

Published In

Volume / Issue

  • 3972 /

Start / End Page

  • 245 - 252

International Standard Serial Number (ISSN)

  • 0277-786X

Citation Source

  • Scopus