FuzzyCmeans
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Contents |
General Information
- Application's name: Parallel Fuzzy C Mean for classification/Feature detection category
- Virtual Research Community: EO-Science
- Scientific contact: Dana Petcu, petcu@info.uvt.ro
- Technical contact: Silviu Panica, silviu@info.uvt.ro
- Developers: Silviu Panica, Daniela Zaharie, West University of Timisoara, Romania ({silviu,dzaharie}@info.uvt.ro)
- Web site: http://research.info.uvt.ro/
Short Description
Fuzzy clustering algorithms allow the identification of spatially continuous regions of pixels characterized by similar feature values, that’s through considering the fact that a pixel in a satellite image may contain spectral information corresponding to different ground components. Since the satellite images are usually large, designing efficient implementation of fuzzy clustering algorithms attracted the interest of researchers. Currently, there exist parallel variants of the traditional Fuzzy C-Means (FCM) algorithm, but their extension to the case of algorithms involving spatial information has not been investigated yet.
This research work aims to extend the existing parallelization of FCM to include some spatial variants (e.g. FCM with spatial information and Gaussian Kernel based FCM). The proposed parallelization is based on three basic ideas: spatial slicing of images, exploiting the collective computations, as much as possible, and reducing the communication between processors. Several slicing strategies were analyzed with respect to their ability to ensure a balanced load of processors. There were also proposed parallel variants for the computation of cluster validity indices useful in the context of semi-automatic identification of the number of classes.
Problems Solved
Fuzzy c-means solves the problem of object clustering in case of remote sensing images. This algorithm tries to identify spatially continuous regions of pixels characterized by similar feature values which most likely corresponds to similar ground cover types, e.g. generate vegetation maps of an area of interest.
Scientific and Social Impact
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Collaborations and Beneficiaries
This work was done in collaboration with:
- IBM Center of Advanced Studies, Egypt
- Ahmed Sayed, asayed72@yahoo.com
- Hisham El-Shishiny, shishiny@eg.ibm.com
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
- Ashraf S. Hussein, ashrafh@acm.org
Technical Features and HP-SEE Implementation
- Primary programming language: C
- Parallel programming paradigm: MPI/MPIX (BlueGene/P)
- Main parallel code: existing code
- Pre/post processing code: local development
- Application tools and libraries: MPICH2, OpenMPI and MPIX
- Number of cores required: 2048
- Minimum RAM/core required: 1GB
- Storage space during a single run: 850MB (average storage; it depends on the input datasets type and size)
- Long-term data storage: 10TB
Usage Example
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Publications
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