Self-Organizing Map based Extended Fuzzy C-Means (SEEFC) algorithm for image segmentation

Display OmittedSOM and EFCM Integration in a Glance View. The motivation for this integration was the difficulties faced in using SOM for Image segmentation. The number of clusters needs to be specified for improving the performance.For different images the map sizes have to be optimized. It was also observed that SOM has limited capabilities for the representation of hierarchical relation of mapped data making interpretation difficult.In this work a novel attempt taking advantage of capabilities of self-organizing map and extended fuzzy clustering for Image segmentation is reported.Incorporating SOM we can find the essential information by mapping image data to a two dimensional space and with the help of EFCM we estimate the number of clusters.Finally the cluster centers are used for clustering purpose and image segmentation. A Novel hybrid algorithm based on Self-Organizing-Map (SOM) and Extended Fuzzy C-Means (EFCM) named self-organizing-map based extended fuzzy c-means (SEEFC) has been designed and implemented for image segmentation. The proposed algorithm works in three stages. At first, the images are decomposed by discrete wavelet transform (DWT) into various frequencies and gradient, pixel value and statistical parameters are obtained to form a feature prototype. The feature prototypes are input to the Self Organizing Map (SOM). Finally, the codebook vectors of the trained SOM have been clustered automatically using Extended Fuzzy C-Means (EFCM). The clustered codebook vector centers are used for image segmentation based on minimum distance criterion. The proposed method has been tested with images from Berkeleys database and results obtained are promising. The segmentation results are evaluated against the ground truth. Comparisons with another state of the art approaches indicate advantages of the SOM based EFCM algorithm.

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