Open Access Open Access  Restricted Access Subscription or Fee Access

Texture Classification and Segmentation using Roughness Feature Extraction through Edge Detection

G. Murugeswari, A. Suruliandi


In this paper, we propose a new rotational invariant roughness feature extraction method for texture image classification and segmentation. Roughness feature extraction is carried out through edge detection, followed by roughness value calculation. We propose an edge descriptor that extracts edge information from an image. We also propose a method for computing the roughness value so to estimate the roughness level of an image. We used the k-nearest neighbor classifier for classification and segmentation. The proposed approach has been tested for rotational variant and illumination variant image classification and texture image segmentation. A few of the existing methods are compared with the proposed method, and it is found that this method outperforms traditional roughness estimation methods.


Texture feature extraction, roughness feature, edge detection, classification, segmentation.

Full Text:



  • There are currently no refbacks.

Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.