Image clustering using fuzzy graph theory

Hamid Jafarkhani; Vahid Tarokh

doi:10.1117/12.373555

23 December 1999 Image clustering using fuzzy graph theory

Hamid Jafarkhani, Vahid Tarokh

Proceedings Volume 3972, Storage and Retrieval for Media Databases 2000; (1999) https://doi.org/10.1117/12.373555
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

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 paris of images in the database and generate the corresponding fuzzy graph. The, 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.

Citation Download Citation

Hamid Jafarkhani and Vahid Tarokh "Image clustering using fuzzy graph theory", Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); https://doi.org/10.1117/12.373555

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