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Image segmentation based on colour and texture features

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  • C. Mythili
  • V. Kavitha

Abstract

A new segmentation approach is proposed in this paper which combines colour texture features to get accurate segmentation. The input images obtained from Berkeley databases are in RGB colour model. The colour image is transformed from RGB colour space to lab colour space. The statistical colour features are extracted from lab colour space. The fuzzy texture unit is determined by the extraction of local texture information from each pixel. The combined feature extraction of colour and texture are implemented using effective robust kernelised fuzzy C-means (ERKFCM) clustering strategy. It is concluded that ERKFCM method has outperformed quantitatively and qualitatively results in terms of root mean square error (RMSE), Pearson correlation coefficient, structural similarity (SSIM) and time taken when compared to the existing methods in segmentation.

Suggested Citation

  • C. Mythili & V. Kavitha, 2016. "Image segmentation based on colour and texture features," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 7(3), pages 272-283.
    • Handle: RePEc:ids:ijenma:v:7:y:2016:i:3:p:272-283
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