On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques

doi:10.1016/0031-3203(90)90103-R

Pattern Recognition

Volume 23, Issue 9, 1990, Pages 935-952

https://doi.org/10.1016/0031-3203(90)90103-R Get rights and content

Abstract

In this paper, a segmentation algorithm for color images based on the thresholding and the fuzzy c-means (FCM) techniques is presented. The scale-space filter is used as a tool for analyzing the histograms of three color components. The methodology uses a coarse-fine concept to reduce the computational burden required for the FCM. The coarse segmentation attempts to segment coarsely using the thresholding technique, while the fine segmentation assigns the pixels, which remain unclassified after the coarse segmentation, to the closest class using the FCM. Attempts also have been made to compare the performance of the proposed algorithm with other existing algorithms—Ohlander's, Rosenfeld's, and Bezdek's. Intensive computer simulation has been performed and the results are discussed in this paper. The simulation results indicate that the proposed algorithm yields the most accurate segmented image on the color coordinate proposed by Ohta et al., while requiring a reasonable amount of computational effort.

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On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques

Abstract

Comput. Graphics Image Process.

Comput. Vision Graphics Image Process.

Comput. Graphics Image Process.

Pattern Recognition

Pattern Recognition

Digital Picture Processing

Digital Image Processing

Digital Image Processing

Computer Vision

Region-based segmentation

Light and Color